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Forward Deployed Engineer – AI Engineer

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Company Overview

The organization is an artificial intelligence research lab focused on developing open AI models that enable businesses and developers to access, customize, and build upon advanced AI capabilities. Its work bridges AI research and practical implementation, helping enterprise customers deploy intelligent applications tailored to real-world business needs.

Job Description

The Forward Deployed Engineer – AI Engineer will be a core contributor to the Applied AI team, leading the development and deployment of AI-powered solutions for enterprise customers. The role involves translating advanced AI research into practical applications and owning the technical strategy and delivery of agentic systems, from initial customer discovery through production launch.

The successful candidate will work closely with customers, deployment strategists, sales teams, and AI researchers to design scalable solutions, integrate AI systems into enterprise infrastructure, and deliver reliable production deployments. This position is suited to an engineer with strong software development skills, hands-on experience with modern AI technologies, and the ability to solve complex business problems in a fast-paced startup environment.

Responsibilities

Understand Enterprise Needs: Collaborate with deployment strategists and sales teams to identify customer requirements, design technical solutions, and develop impactful agentic applications.
Build Agentic Systems: Develop AI-powered systems using advanced models, orchestrate large language model (LLM) workflows, and integrate solutions with enterprise infrastructure.
Deliver Production-Ready Applications: Build and deploy reliable, scalable AI systems that meet enterprise performance and operational requirements.
Collaborate on Model Adaptation: Work with research teams to adapt and fine-tune models for specific customer use cases.
Manage End-to-End Deployments: Support implementations across public cloud, virtual private cloud (VPC), and on-premises environments, ensuring scalability, performance, and reliability.
Integrate Modern AI Technologies: Apply vector databases, retrieval-augmented generation (RAG), agent orchestration, evaluation frameworks, and model fine-tuning to solve customer problems.
Support Engineering Excellence: Contribute to deployment playbooks, processes, and best practices as the forward deployed engineering function grows.
Drive Customer Outcomes: Translate business requirements into effective technical solutions and deliver measurable value through AI implementation.

Qualifications

Strong software engineering background with experience delivering production-grade systems using Python and TypeScript.
Proven experience deploying enterprise software in cloud or hybrid environments using modern DevOps practices.
Hands-on experience with Docker, Kubernetes, and continuous integration and continuous delivery (CI/CD) workflows.
Strong understanding of machine learning concepts and modern AI technology stacks.
Experience with vector databases, RAG pipelines, agent orchestration, AI evaluation, and model fine-tuning.
At least 3 years of software engineering experience delivering AI-driven enterprise solutions, such as in a Forward Deployed Engineer, Software Engineer, or Applied AI Engineer role.
Demonstrated ability to work directly with customers, understand user needs, architect solutions for business challenges, and deliver tangible outcomes.
Self-motivated approach with strong ownership, initiative, and adaptability.
Ability to thrive in a fast-paced startup environment where processes and engineering practices are still evolving.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Machine Learning Engineer, Safety

Job Title:

Location: San Francisco, California

Employment Type: Full-time

Location Type: On-site

Department: Engineering

Compensation: $180,000–$250,000 per year, plus equity

Company Overview

The organization develops infrastructure, tools, and machine learning capabilities that enable developers and enterprises to build and deploy generative AI applications at scale. Its platform supports high-performance inference, model orchestration, and observability for AI-powered products, including generative media applications.

Job Description

The Machine Learning Engineer, Safety will own the machine learning models and infrastructure that power the organization’s safety systems. This hands-on engineering role focuses on developing and maintaining models that detect harmful content, platform misuse, and abusive activity, along with the production pipelines required to operate these systems reliably at scale.

Working within the Trust and Safety function and collaborating with security and infrastructure engineering teams, the engineer will improve detection accuracy, coverage, latency, and scalability across a platform supporting more than 1,000 models. The role requires strong machine learning and infrastructure engineering skills, practical experience with trust and safety systems, and the ability to solve complex, high-stakes problems in a rapidly evolving environment.

Responsibilities

Design, build, and maintain machine learning models and infrastructure for safety and abuse-detection systems from development through production deployment.
Improve the accuracy, coverage, latency, and scalability of detection pipelines across the platform.
Collaborate with security and infrastructure engineering teams to integrate safety systems into core platform infrastructure.
Evaluate and integrate third-party safety tools and vendor models where appropriate.
Monitor developments in machine learning safety and detection, applying new techniques and infrastructure approaches to improve existing systems.
Build and operate reliable machine learning inference and evaluation workflows using large-scale GPU infrastructure.
Use technologies such as Python, PyTorch, Diffusers, Kubernetes, and relevant Python SDKs to develop and deploy safety capabilities.
Support rapid experimentation and deployment of new AI technologies while maintaining robust safety standards.

Qualifications

Required: Hands-on experience in trust and safety, content moderation, or abuse-detection systems.
Strong end-to-end engineering fundamentals, with the ability to own both machine learning models and the infrastructure that serves them in production.
Experience building, deploying, and maintaining reliable machine learning systems.
Ability to take ownership of ambiguous, high-stakes technical challenges with limited precedent.
Familiarity with Python and machine learning frameworks and infrastructure technologies relevant to production AI systems.
Ability to collaborate effectively with security, infrastructure, and engineering teams.
Willingness to work in person in San Francisco five days per week.

Work Environment

The position is based in San Francisco and follows a fully on-site, five-day-per-week work arrangement. The engineer will work closely with a dedicated safety engineering team in a fast-paced environment focused on deploying advanced AI capabilities responsibly.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Senior Data Scientist

Company Overview

The organization develops AI and machine learning solutions designed to address operational challenges in healthcare, including staffing inefficiencies and administrative burdens. Its technology combines conversational AI, voice-based analysis, and machine learning to improve healthcare delivery and operational efficiency.

Responsibilities

Lead High-Impact Projects: Own data science and AI evaluation projects end-to-end, including defining problems, developing methodologies, conducting analyses, communicating findings, and driving implementation.
Improve LLM-as-a-Judge Systems: Build data and evaluation infrastructure for a closed-loop AI quality system, including golden datasets, ground-truth benchmarks, judge calibration, error analysis, and production feedback.
Identify and Address Failure Modes: Translate evaluation insights into improvements to datasets, evaluation methodologies, LLM-as-a-Judge systems, and underlying AI products.
Develop Evaluation Frameworks: Design and implement reliable evaluation methodologies and metrics for large language model (LLM) and speech-based AI systems.
Build and Maintain Evaluation Datasets: Create, curate, and maintain high-quality datasets for training, testing, benchmarking, and evaluating conversational AI systems and their components.
Develop Data Pipelines and Dashboards: Build and maintain scalable data pipelines and intuitive dashboards to support reliable analysis, ongoing AI quality monitoring, and data-driven decision-making.
Collaborate Across Teams: Work with AI/ML engineers and product managers to translate findings into product improvements and contribute to technical strategy.
Stay Current with AI Advances: Track developments in AI evaluation, particularly in LLMs and speech technologies, and apply relevant techniques to improve system quality.
Mentor and Lead: Guide junior data scientists, establish technical best practices, and strengthen the team’s overall technical capabilities.

Qualifications

At least 5 years of industry experience in data science, machine learning, or a related quantitative field.
Bachelor’s or master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.
Strong Python programming skills, including experience with libraries such as Pydantic, Pandas, NumPy, and Scikit-learn.
Solid foundation in statistical analysis, experimental design, hypothesis testing, and A/B testing methodologies.
Experience working with large-scale datasets, data pipelines, and analytical tools to generate actionable insights.
Familiarity with evaluation metrics, including precision, recall, and agreement metrics such as Cohen’s Kappa, to calibrate judge models against human ground truth.
Demonstrated ability to independently lead projects from problem definition through execution and implementation.
Excellent communication and interpersonal skills, with the ability to collaborate effectively with AI/ML engineers, product managers, and cross-functional stakeholders in a fast-paced environment.

Preferred Qualifications and Additional Skills

Experience with natural language processing (NLP), LLM architectures, and the challenges involved in evaluating language models.
Experience with speech technologies and speech-system evaluation.
Background in a regulated industry, such as healthcare or finance.
Familiarity with data governance principles, particularly those related to personally identifiable information (PII) and protected health information (PHI).
Experience with MLOps practices and deploying machine learning models to production.
Familiarity with Langfuse and cloud platforms such as Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure.
Experience with data platforms such as Databricks.

Work Arrangement

The position is based in the San Francisco Bay Area with a hybrid work arrangement. Remote candidates may also be considered if they are located within the United States.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

AI Engineer

Job Title:

Location: San Francisco, California

Employment Type: Full-time

Location Type: On-site

Department: Product

Company Overview

The organization is an artificial intelligence company based in San Francisco, California, focused on developing advanced AI systems and autonomous agents. Its work combines AI research, machine learning engineering, and software development to build intelligent systems capable of performing complex tasks across desktop environments, web browsers, and enterprise data sources.

Job Description

The AI Engineer will contribute to the development of agentic systems and interactive AI applications. The role focuses on building next-generation desktop and browser-based agents that can navigate the web, interact with filesystems, and complete complex user tasks autonomously.

The position spans frontend interfaces, secure sandboxing environments, large-scale document search and retrieval, and language and vision model integration. The ideal candidate is motivated to build production-grade machine learning systems, thrives in fast-moving and ambiguous environments, and is eager to explore new approaches to autonomous software agents.

Responsibilities

Design and implement agentic systems capable of interacting with web browsers, operating systems, and enterprise filesystems.
Build search and retrieval pipelines for large-scale structured and unstructured data.
Integrate large language models (LLMs), vision models, reinforcement learning, and agent orchestration frameworks to support autonomous, multi-step decision-making.
Develop secure, virtualized runtime environments and backend services for agent execution.
Work across the machine learning stack, from model integration to serving infrastructure.
Build reliable, production-ready AI systems that expand the capabilities of autonomous software agents.
Collaborate with product, research, and engineering teams to deliver innovative AI solutions.
Explore new ideas and approaches while maintaining a focus on engineering quality and practical impact.

Qualifications

Proficiency in Python and a strong understanding of building and debugging complex machine learning applications.
Experience working with desktop operating systems, particularly Windows and macOS, including APIs for screen reading, file interaction, and accessibility frameworks.
Experience developing browser extensions or automation tools with fine-grained control over browser interactions, including mouse actions, tabs, and the Document Object Model (DOM).
Understanding of LLMs, prompting techniques, and orchestration frameworks for multi-step reasoning.
Ability to work across the full machine learning stack, including model integration and serving infrastructure.
Experience designing or working with secure and virtualized execution environments.
Strong communication and collaboration skills when working across product, research, and engineering teams.

Preferred Qualifications and Additional Skills

Experience building or integrating retrieval-augmented generation (RAG) systems.
Familiarity with enterprise security and compliance frameworks, such as SOC 2.
Experience with vector databases and large-scale document indexing.
Knowledge of web automation tools and headless browser environments, such as Puppeteer and Playwright.
Understanding of sandboxed or containerized computing environments with strict access controls.
Experience designing user-facing agentic workflows and reasoning systems involving multiple modalities, including text, vision, and actions.
Experience using or fine-tuning models for screen reading, optical character recognition (OCR), or user interface understanding.
Background in human-computer interaction (HCI) or a strong interest in building intuitive agent interfaces that extend human capabilities.

Work Environment and Culture

A research-driven approach that emphasizes methodical progress toward ambitious goals.
Equal appreciation for deep research and engineering excellence.
An environment that encourages unconventional ideas, experimentation, and bold technical initiatives.
A fast-moving culture designed to help team members make meaningful contributions quickly.
A collaborative atmosphere where team members enjoy exploring and discussing artificial intelligence.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Data Infrastructure Engineer

Company Overview

The organization develops physical AI and robotics solutions for real-world industrial applications, beginning with factory environments. Its engineering teams focus on enabling robots to learn from real-world experience by transforming raw, multimodal sensor streams and human demonstration data into high-quality datasets for machine learning and model training.

Job Description

The Data Infrastructure Engineer will build and operate the data pipelines and infrastructure that transform raw sensor and demonstration data into curated, training-ready datasets at scale. The role spans data ingestion from robots and capture devices, data processing and quality filtering, multimodal storage and retrieval, and the development of data loaders that support model training.

The successful candidate will take ownership of specific pipelines and services within an established architecture, troubleshoot production issues, and strengthen system reliability as data volumes and operational complexity grow. Close collaboration with research and modeling teams will be essential to ensure that data infrastructure meets evolving model training requirements.

Responsibilities

Build and scale ingestion pipelines for high-volume, high-dimensional sensor data.
Develop and maintain data quality and curation systems, including manual review workflows and automated labeling.
Contribute to storage and retrieval architectures for large-scale multimodal datasets, including data format selection, versioning, and lineage tracking.
Build and operate distributed data processing infrastructure for both batch and streaming workloads.
Troubleshoot production data pipeline issues, including data corruption, schema drift, backpressure, and failures that emerge at scale.
Collaborate with research and modeling teams to understand data quality, format, and structure requirements and translate them into reliable pipelines.
Participate in system design discussions and code reviews across the data infrastructure stack.
Improve pipeline reliability, scalability, and maintainability as the data platform expands.

Qualifications

At least 2 years of software engineering experience, with exposure to data pipelines, data engineering, or backend systems.
Strong Python programming fundamentals and the ability to develop performant, production-grade data processing code.
Experience with at least one distributed data processing framework, such as Spark, Ray, Dask, or Flink, or strong foundational knowledge and the ability to learn these technologies quickly.
Familiarity with data storage concepts, including object storage, data lake table formats, and the trade-offs between data warehouses and data lakes.
Demonstrated ownership of features or systems from prototype through production.
Strong communication and collaboration skills, particularly when working with research and modeling partners and senior engineering teammates.

Preferred Qualifications

Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
Experience building streaming ingestion pipelines for high-volume, near-real-time data.
Experience developing automated data quality and curation systems, including statistical filtering, anomaly detection, deduplication, or machine learning-based annotation and filtering.
Experience working with robotics or embodied AI datasets, including multi-embodiment data, teleoperation and demonstration data, or egocentric video.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Cloud Engineering Manager

Company Overview

The organization is developing an AI-powered platform for wealth management professionals, helping financial advisors improve operational efficiency, manage resources, and deliver better client outcomes. Its technology supports financial services operations where security, reliability, regulatory compliance, and scalable infrastructure are essential.

Responsibilities

Team Leadership and Engineering Strategy

Lead, coach, and develop cloud, DevOps, and database reliability engineers across local and offshore teams.
Conduct regular one-on-one meetings, establish career development plans, manage performance, and support recruitment.
Own the Cloud Engineering roadmap and guide the delivery of strategic infrastructure initiatives.
Make architectural decisions that shape the organization’s AWS infrastructure and support long-term scalability and reliability.

Cloud Infrastructure and Reliability

Build and maintain secure, highly available, observable infrastructure that enables product teams to deploy and operate services independently.
Oversee a governed Amazon Elastic Kubernetes Service (EKS) platform supporting broker-dealer and clearing systems.
Lead disaster recovery initiatives targeting a recovery time objective (RTO) of 60 minutes or less and a recovery point objective (RPO) of 15 minutes or less.
Coordinate PostgreSQL upgrades and edge gateway consolidation.
Improve infrastructure reliability, performance, and resilience across production environments.

Qualifications

Experience and Technical Expertise

At least 6 years of experience in cloud infrastructure, DevOps, or Site Reliability Engineering.
At least 2 years of experience managing engineers within a B2B financial services, fintech, or SaaS organization.
Deep expertise in AWS networking, identity and access management (IAM), compute, storage, and production-scale cost management.
Strong experience with Kubernetes, particularly EKS, and Infrastructure as Code tools such as Terraform and Helm.
Experience with GitOps delivery using Argo CD.
Hands-on ownership of CI/CD pipelines, including GitLab, artifact management, secrets management with Vault, and release safety practices.
Experience operating production on-call rotations and serving as a senior escalation point for technical incidents.
Familiarity with OpenTelemetry, SLOs, and error budgets for operational decision-making.

People Leadership and Professional Skills

Proven ability to hire, develop, and retain engineers, including remote and offshore team members.
Strong ownership, accountability, and technical judgment.
Excellent communication and collaboration skills, including the ability to listen effectively and communicate complex technical decisions.
Resilience and adaptability when addressing challenging operational and engineering problems.
Creative problem-solving skills and the ability to identify practical solutions to technical roadblocks.
Ability to learn and work effectively with modern technology stacks, including Kubernetes, Kafka, Temporal, PostgreSQL, and Snowflake.
A bachelor’s degree in Computer Science, Software Engineering, or a related field is preferred, although equivalent practical experience is also valued.

Preferred Qualifications

Experience with AI engineering best practices, including Claude, and deploying AI-assisted workflows across engineering teams.
Experience operating in regulated environments, including SOC 1, SOC 2, FINRA, or SEC examinations, and automating compliance evidence.
Hands-on experience with Kong, Envoy, or Gateway API for edge infrastructure.
Advanced PostgreSQL operational experience, including upgrades, replication, change data capture, and performance tuning.
Familiarity with Teleport, Vault, Karpenter, and policy engines such as OPA Gatekeeper or Kyverno.
Experience conducting disaster recovery tabletop exercises and GameDays.
Knowledge of brokerage platforms and financial services operations.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

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Forward Deployed AI Strategy Lead

Company Overview

The organization develops an open, full-stack artificial intelligence infrastructure platform designed to help AI research teams, enterprises, and technology companies build, train, evaluate, and deploy advanced AI systems. Its platform supports compute infrastructure, evaluation environments, secure sandboxes, high-performance training, and deployment workflows, including supervised fine-tuning, reinforcement learning, tool use, and agent development.

Responsibilities

Strategic Customer Deployments

Lead high-priority customer engagements from initial technical discovery through proof of concept, deployment, expansion, and case study development.
Work with customers building AI agents, automating complex workflows, improving model performance, reducing inference costs, developing domain-specific evaluations, and implementing large-scale post-training.
Help customers determine which tasks to train or evaluate, how to define success, and which workflows should become structured AI environments.
Identify required data, execution traces, automation opportunities, supervision requirements, and measurement approaches.
Recommend suitable model adaptation strategies and define pathways from prototype development to production deployment.

Technical Discovery and Project Scoping

Translate ambiguous customer requirements, internal documentation, product objectives, and technical constraints into clearly defined implementation plans.
Define use cases, success metrics, evaluation strategies, environment requirements, and integration needs.
Establish project milestones, commercial structures, risks, dependencies, and potential expansion opportunities.
Ensure that technical scopes are actionable and aligned with the capabilities of Applied Research and Engineering teams.

Applied Research Collaboration

Partner closely with Applied Research to translate customer feedback and real-world requirements into research priorities and product roadmap opportunities.
Identify evaluation methods, environments, agents, and post-training approaches that can advance AI capabilities while delivering measurable customer outcomes.
Explore methods for converting real-world workflows into reliable reinforcement learning environments.
Assess evaluation quality, verifier reliability, task trainability, and the effectiveness of managed reinforcement learning compared with prompting or manual workflow design.
Develop approaches to demonstrate measurable model performance improvements to customers.

Repeatable Deployment and Commercial Strategy

Develop repeatable processes that make strategic customer deployments more efficient and scalable.
Create discovery templates, customer qualification frameworks, proof-of-concept structures, and deployment playbooks.
Contribute to proposals, pricing and packaging strategies, reference architectures, case studies, and technical narratives.
Document successful deployment approaches and turn lessons learned into reusable processes.
Help establish a scalable operating model for delivering applied AI solutions and expanding customer relationships.

Qualifications

The provided job description does not specify formal degree requirements, minimum years of experience, or mandatory certifications. Candidates should be prepared to demonstrate relevant capabilities in the following areas:

Ability to translate complex business workflows into clearly scoped AI implementation opportunities.
Understanding of AI systems, model evaluation, post-training, and agent-based workflows.
Ability to define measurable success criteria and develop structured approaches to evaluating AI performance.
Experience coordinating technical deployments across customer-facing, research, and engineering teams.
Strong problem-solving skills and the ability to navigate ambiguous requirements and technical constraints.
Commercial awareness, including the ability to connect technical outcomes with customer value, revenue opportunities, and long-term expansion.
Strong written and verbal communication skills for customer discovery, technical scoping, proposals, and stakeholder alignment.
Ability to develop repeatable frameworks, documentation, and deployment processes that improve future engagements.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Lead AI Engineer

Company Overview

The organization is a global consumer cybersecurity provider focused on protecting individuals, families, and communities through integrated digital safety products. Its eCommerce platform supports millions of customers worldwide across purchasing, checkout, subscription management, and automatic renewal processes. The engineering organization is developing operational intelligence and AI-driven productivity capabilities to improve system reliability, commercial performance, and engineering efficiency.

Responsibilities

Data Engineering and Platform Development

Integrate data from eCommerce, marketing technology, observability, and operational databases into analytics-ready and AI-ready datasets using production ETL/ELT pipelines, advanced SQL, and deliberate data modeling.
Implement automated data quality validation to ensure data reliability and consistency.
Design dimensional data models, including star and snowflake schemas, with appropriate data grain and partitioning strategies for analytics, KPI reporting, and dashboards.
Build summarized gold-layer datasets that consolidate millions of raw events into clean, entity-level daily records to support reliable natural-language querying and executive reporting.
Develop and optimize scalable data processing workflows using Spark and PySpark.

Operational Intelligence and Performance Monitoring

Design and deliver a unified eCommerce Health platform that combines system performance indicators, including latency, errors, integrations, and data-flow health, with commercial metrics such as conversion, checkout performance, subscription renewals, and feature adoption.
Standardize engineering metrics across teams, covering delivery performance, software quality, security posture, incident trends, and service ownership.

Qualifications

Application Development and Data Engineering

At least 8 years of application development experience, including the design and architecture of large-scale production applications and distributed systems.
At least 5 years of hands-on production data engineering experience, building pipelines, ETL/ELT workflows, and analytics platforms that transform raw operational and business data into actionable insights.
Demonstrated expertise in business-critical data engineering and the ability to apply that expertise across different domains and data platforms.
Expert-level Spark and PySpark skills, including performance tuning, Spark SQL, partitioning, and shuffle optimization at billion-row scale.
Expert SQL skills for large-scale data processing and strong Python programming capabilities.
Strong data modeling expertise, including dimensional modeling, star and snowflake schemas, data grain, and partitioning strategies.
Experience with modern lakehouse platforms, with deep Databricks experience strongly preferred.
Familiarity with Delta Lake, Databricks Workflows, Unity Catalog, Medallion architecture, and real-time or event-driven data processing.

AI Engineering

Hands-on experience delivering production LLM-powered applications, including RAG systems, AI agents, or LLM-integrated internal tools.
Sound judgment in the use of AI tools, guardrails, evaluation methods, and reliability practices.
Experience personally building at least one AI agent and explaining its monitoring capabilities, decision-making process, framework, model selection, and handling of incorrect outputs.
Regular use of AI coding assistants such as Claude, GitHub Copilot, or comparable tools to accelerate practical engineering work.

Domain Knowledge

Experience in eCommerce or a comparable operational domain involving funnel metrics, subscription workflows, payment processing, or transactional systems.
Understanding of transactional and operational data structures and how to model them for analytics and decision-making.

Professional Skills

Demonstrated ability to use AI to measurably reduce repetitive work and accelerate incident response.
Strong cross-functional collaboration skills, particularly with Product, SRE, Security, and Architecture teams.
A hands-on, efficiency-driven approach to engineering, with a focus on reliable production systems and measurable business impact.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

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AI Engineer, Manager – Tax Transformation

Company Overview

The organization provides professional tax and advisory services supported by enterprise technology and software engineering. Its tax transformation team develops and implements innovative tools, applications, and standardized processes to improve service delivery, operational efficiency, and technology-enabled tax solutions. The team brings together professionals from tax, technology development, change management, and project management.

Responsibilities

Define and enforce engineering best practices, coding standards, and project development guidelines.
Conduct code reviews to maintain high code quality and ensure adherence to established standards.
Work independently and collaboratively with engineering team members to deliver project objectives.
Communicate clear instructions, coordinate daily operations, and support effective team execution.
Maintain regular communication with clients and relevant stakeholders.
Design, develop, and maintain robust, scalable Python applications.
Write clean, maintainable, and efficient code in accordance with engineering best practices.
Optimize application performance, scalability, and data-handling efficiency.
Collaborate with cross-functional teams to deliver high-quality software solutions.
Identify and resolve technical issues to maintain application reliability and performance.
Create and maintain comprehensive documentation covering code, processes, and workflows.
Mentor junior developers and promote a collaborative, productive engineering environment.

Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related discipline.
At least 5 years of software development experience, including designing, developing, and maintaining robust Python applications.
Hands-on experience with FastAPI web development and Pydantic for data validation and schema definition.
Practical experience with asynchronous and parallel programming using asyncio.
Experience working in Agile environments and applying core software design patterns.
Demonstrated proficiency in .NET Core, ASP.NET Core Web API, and SQL or NoSQL databases, including Entity Framework 6 or later.
Ability to work within a hybrid arrangement requiring in-person collaboration 2–3 days per week.
Willingness to travel up to 10% on average, depending on project and client requirements.
Strong technical communication, collaboration, and problem-solving skills.

Required Professional License or Certification

Candidates must hold at least one applicable active accreditation:

Licensed Certified Public Accountant (CPA) in the relevant state of practice or primary office, if eligible for the CPA examination.
For candidates who are not CPA-eligible, one of the following:
- Licensed Attorney
- Enrolled Agent
- AWS Certified Solutions Architect
- Certified in Risk and Information Systems Controls (CRISC)
- Certified Information Systems Security Professional (CISSP)
- Certified SAFe Advanced Scrum Master
- Certified SAFe Agile Software Engineer
- Certified SAFe Agilist
- Certified SAFe Architect
- Certified SAFe DevOps Practitioner
- Certified SAFe Scrum Master
- Certified Scrum Developer (CSD)
- Certified Secure Software Lifecycle Professional (CSSLP)
- Microsoft Azure certification

Preferred Qualifications

Experience applying large language models (LLMs) to real-world problems and building agentic AI applications.
Familiarity with agentic AI frameworks such as LangGraph.
Advanced prompt engineering skills for LLM optimization.
Exposure to multimodal generative AI models, including text-image and text-audio systems.
Familiarity with Retrieval-Augmented Generation (RAG) pipelines, vector databases, and hybrid search.
Experience with performance tuning, reusable library development, and advanced troubleshooting.
Familiarity with Angular, MongoDB, NPM, and Azure DevOps build and release configuration.
Strong verbal and written communication, active listening, interpersonal, and facilitation skills.
A proactive approach with strong analytical and problem-solving abilities.
Ability to balance standardized processes with flexibility to achieve project goals.
Excellent organizational skills, including the ability to manage priorities, structure workloads, and meet tight deadlines.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Applied ML Engineer

Company Overview

The organization is an early-stage technology venture focused on building next-generation AI-native products. Backed by an experienced entrepreneur with a track record of founding and scaling technology companies, the venture is assembling a high-caliber engineering team to develop impactful AI solutions and bring innovative products to market.

Job Description

The Applied ML Engineer will develop, deploy, and support AI-native products, applying strong machine learning expertise to build reliable, high-performing production systems. The role requires sound technical judgment, awareness of emerging machine learning research, and the ability to translate new developments into practical product capabilities.

The successful candidate will collaborate with experienced engineers, contribute to engineering decisions, and mentor other machine learning professionals while helping shape the technical direction and culture of an early-stage company.

Responsibilities

Develop, deploy, and support AI-native products from initial concept through production.
Apply sound technical judgment to the design, development, and maintenance of high-performing production machine learning systems.
Stay informed about the latest machine learning research, techniques, and industry developments.
Collaborate with a team of experienced engineers to deliver reliable and impactful AI solutions.
Mentor and support other machine learning engineers, contributing to their professional growth and technical development.
Help establish engineering practices and technical standards that support a high-performing engineering team.

Qualifications

At least 5 years of experience delivering and supporting production AI and machine learning products.
An advanced degree in Machine Learning, Theoretical Computer Science, Mathematics, Physics, or a related field, or equivalent experience demonstrated through meaningful contributions to open-source AI/ML projects or the development of successful AI products.
Strong decision-making skills in large-scale machine learning system design, deployment, and maintenance.
A solid understanding of current machine learning research, emerging techniques, and technological advancements.
A strong interest in collaborating with talented engineers, continuous learning, and helping build a high-caliber engineering team.
A proven track record with organizations known for strong engineering cultures, including meaningful experience at startups.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
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ML Scientist I/II, AI for Protein Engineering

Company Overview

The organization is developing an AI-driven platform that combines artificial intelligence and automation to address complex challenges in medicine and life sciences. Its Life Sciences AI team develops generative and predictive models to advance biomolecular design, connecting computational predictions with experimental validation to accelerate therapeutic discovery.

Job Description

The ML Scientist I/II will focus on applying machine learning to protein engineering, supporting active protein design programs and developing technologies that improve the design, evaluation, and learning processes for biomolecular sequence, structure, and function data.

This role combines machine learning, protein engineering, and therapeutic design. The successful candidate will apply strong machine learning fundamentals and an understanding of protein biology to develop computationally designed, experimentally validated biologics. The position involves collaboration with experimental scientists, AI researchers, and platform engineering teams to build models and workflows that contribute to an autonomous science platform.

Responsibilities

Build machine learning workflows for protein engineering campaigns, from design specification through experimental learning.
Develop and adapt methods for de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning.
Integrate protein design methods into robust software systems and broader reasoning models.
Translate therapeutic and biological questions into well-defined machine learning problems, model outputs, and evaluation plans.
Collaborate with experimental scientists to understand why designed biomolecules succeed or fail and translate findings into model improvements.
Develop evaluation frameworks to assess model generalization across challenging biologics design problems.

Qualifications

Ph.D. in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field.
Experience applying machine learning to protein design, biologics engineering, or related biomolecular design problems.
Strong machine learning fundamentals, with hands-on experience developing, adapting, training, or evaluating modern AI methods.
Familiarity with biological sequence, structure, function, developability, or experimental validation considerations.
Ability to translate therapeutic or biological objectives into computational design problems and model evaluation plans.
Strong collaboration and communication skills across machine learning, biology, experimental science, and software teams.

Preferred Qualifications

Experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins.
Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models.
Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or other biophysical constraints.
Experience managing design-test-learn cycles with wet-lab teams, including experimental prioritization, high-throughput validation, and active learning.
Industry experience translating machine learning research into practical biological design workflows, experimental campaigns, or platform capabilities.
Publications, open-source contributions, or applied research outputs in AI for science.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
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Applied AI Engineer

Company Overview

The organization is developing next-generation cybersecurity products designed to help security teams detect, investigate, and respond to threats more effectively. Its team brings together experienced industry professionals focused on building innovative security technologies and delivering impactful solutions. The company offers an opportunity to contribute to foundational product development while growing professionally alongside a team of experienced engineers and researchers.

Job Description

The Applied AI Engineer will develop advanced artificial intelligence capabilities for a next-generation cybersecurity platform. Working at the intersection of AI and cybersecurity, the engineer will design agentic AI systems, build machine learning solutions, and train deep learning and large language models (LLMs) to support threat detection, investigation, and response at scale.

The role involves close collaboration with security researchers and software engineers to develop, productize, and deploy AI-driven solutions across Security Operations Center (SOC), Security Orchestration, Automation, and Response (SOAR), and Security Information and Event Management (SIEM) workflows.

Responsibilities

Develop advanced agentic AI systems for next-generation SOC, SOAR, and SIEM platforms.
Build and optimize large language models for cybersecurity and security operations through post-training techniques.
Develop domain-specific evaluation frameworks and benchmarks to improve model quality, accuracy, and efficiency.
Design and implement machine learning and deep learning solutions for security threat detection, including malware, phishing, anomaly detection, and attack activity classification.
Develop AI workflows and model pipelines that leverage large-scale security datasets.
Collaborate with security researchers to improve data quality, model evaluation, and cybersecurity AI capabilities.
Partner with software engineers to integrate AI models into scalable, production-ready security products.
Research and apply advancements in LLM architectures, AI agents, model inference, and serving optimization.

Qualifications

At least 5 years of experience applying machine learning, deep learning, or generative AI to cybersecurity or related domains, and/or a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
Proven experience applying machine learning, deep learning, or generative AI techniques and delivering high-quality models and solutions into production environments from end to end.
Deep understanding of modern AI agent architectures, agent frameworks, and implementation patterns.
Experience with LLM supervised fine-tuning (SFT), reinforcement learning, preference optimization, and model distillation.
Strong understanding of supervised and unsupervised learning, reinforcement learning algorithms, model training, and evaluation using large-scale datasets.
Experience with domain-specific feature engineering, data labeling, and data quality optimization.
Strong proficiency in Python and experience with deep learning frameworks such as PyTorch.
Familiarity with databases, large-scale data processing systems, and production machine learning pipelines.
Strong communication and technical documentation skills.

Preferred Qualifications

Experience with LLM architecture, model pre-training, inference optimization, and model-serving infrastructure.
Experience with big data streaming pipelines, large-scale analytics, and distributed data processing.
Previous experience applying AI to cybersecurity domains, including threat detection, anomaly detection, fraud detection, malware classification, network traffic analysis, webpage classification, or security operations.
Contributions to AI/ML competitions, hackathons, open-source projects, technical blogs, arXiv, or leading research publications.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Forward Deployed Engineer

Company Overview

The company develops AI-native solutions that help regulated financial institutions streamline back-office operations. Its platform supports compliance, risk management, fraud prevention, and operational workflows using organization-specific data and policies. The technology emphasizes traceability, cited outputs, and audit-ready processes to meet the requirements of highly regulated environments.

About the Role

The Forward Deployed Engineer will work directly with customers to deploy and integrate AI technology in complex enterprise environments. The role combines software engineering, customer implementation, and product discovery, helping organizations integrate AI systems into their existing infrastructure while identifying opportunities to improve and scale the platform.

Responsibilities

Deploy AI agents: Implement AI solutions for financial institutions and related organizations while navigating security, connectivity, and compliance requirements.
Manage customer integrations: Work on-site with customers to resolve integration challenges, connect core systems, and address issues that arise in live production environments.
Translate business workflows: Collaborate with compliance, anti-money laundering, risk, and operations teams to translate business processes and regulatory requirements into effective AI agent behavior.
Build reusable capabilities: Develop connectors, configuration tools, and internal frameworks that turn customer-specific implementations into repeatable platform features.
Guide customer deployments: Support customers from initial demonstrations through production launch, acting as a trusted technical advisor and helping them maximize the value of the platform.
Collaborate cross-functionally: Work with engineering, product, and go-to-market teams to remove implementation blockers and ensure successful customer outcomes.
Troubleshoot production issues: Reproduce, diagnose, and resolve technical problems in live deployments in collaboration with engineering teams.
Identify product opportunities: Communicate customer feedback and usage patterns to product and engineering teams, identifying recurring needs that can be addressed through new platform features.

Technical Qualifications

At least 2 years of experience as a software engineer or in a similar technical role.
Strong background in backend software engineering or data engineering.
Experience with Python, APIs, data pipelines, and cloud infrastructure.
Active hands-on experience building with large language models (LLMs) and modern AI systems.
Previous experience in a customer-facing technical role, such as solutions engineering, forward deployed engineering, or technical implementation, is an advantage.

Soft Skills

Excellent written and verbal communication skills.
Ability to explain AI systems and technical concepts to non-technical stakeholders.
Strong debugging, analytical, and problem-solving skills.
Confidence working directly with enterprise customers in complex technical environments.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Forward Deployed Data Engineer

Company Overview

The company provides a modern technology platform for grocery retailers, helping them improve inventory management, reduce product shortages and waste, and streamline daily operations. Its platform integrates data from various retail systems to support more efficient, data-driven business processes.

Job Description

The Forward Deployed Data Engineer will bridge the gap between customer systems and the company’s grocery technology platform. Working closely with onboarding managers and customer technical teams, the engineer will configure integrations to meet each retailer’s unique operational requirements.

As one of the founding members of the Forward Deployed Data Engineering team, the engineer will help establish processes, playbooks, data pipelines, and integration systems that support the company’s growth from its initial customers to thousands of stores.

The role involves hands-on data engineering, including processing item files, invoices, and other records from point-of-sale systems, back-office tools, and enterprise resource planning systems. Data may arrive in formats such as CSV, EDI, PDFs, and scanned documents. The engineer will develop reliable methods to extract, transform, reconcile, and integrate this information into the platform.

This position is suited to a detail-oriented engineer who enjoys solving complex data integration challenges, collaborating directly with customers, and building scalable systems in a fast-paced environment.

Responsibilities

Develop a comprehensive understanding of the platform’s architecture and the interdependencies between its core systems and integration components.
Understand customer requirements and translate them into technical specifications and system configurations.
Configure and maintain data mappings, validation processes, and reconciliation workflows as part of the operations function.
Collaborate with Operations, Product, Engineering, and customer IT teams to improve workflows and systems from end to end.
Build and maintain data extraction, ingestion, and transformation pipelines for diverse customer data sources and formats.
Troubleshoot complex integration issues and implement reliable solutions in demanding customer environments.
Establish repeatable integration processes and documentation to support onboarding efficiency and long-term scalability.
Identify opportunities to improve data quality, pipeline performance, and integration reliability.

Qualifications

At least 5 years of experience in Forward Deployed Engineering, Solutions Engineering, or a similar customer-facing technical role, ideally within B2B SaaS or fintech.
Strong backend and data engineering fundamentals, combined with the ability to communicate directly with customer IT teams.
Familiarity with unstructured data, ETL processes, and machine learning or optical character recognition (OCR) pipelines.
Strong SQL, regular expression (Regex), and scripting skills, with a willingness to learn new technologies.
Ability to troubleshoot complex technical problems in high-pressure customer environments.
Ability to independently manage data extraction, ingestion, integration, and pipeline optimization with minimal guidance.
Strong attention to detail and a practical approach to resolving data quality and integration challenges.
Effective cross-functional communication and collaboration skills.

Growth Opportunities

Develop expertise in complex data systems while gaining a deeper understanding of the operational and commercial challenges facing the grocery industry.
Solve unique, real-world technical problems that strengthen problem-solving skills and encourage creative thinking.
Take ownership of foundational data integration processes and help establish scalable engineering practices.
Contribute to technology that supports efficiency and modernization across a large, essential industry.

Role Expectations

Work effectively within a fast-paced startup environment with broad responsibilities and demanding deadlines.
Demonstrate initiative, adaptability, and consistent ownership when tackling complex technical and operational challenges.
Balance speed and creativity with reliability, data quality, and customer outcomes.
Contribute to a high-performance team focused on building impactful technology with limited resources.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Forward Deployed Engineer

Company Overview

The organization develops AI-powered solutions that help essential services businesses modernize operations, automate complex workflows, and increase revenue. Its platform enables businesses to adopt AI-first operating models, bringing advanced artificial intelligence into real-world service industries. The team consists of experienced engineers and builders focused on solving complex technical challenges and delivering measurable business impact.

Job Description

The Forward Deployed Engineer plays a key role in building and deploying AI systems that transform how essential services businesses operate. This position combines advanced AI engineering, customer collaboration, and end-to-end product implementation. The engineer develops autonomous AI agents capable of handling complex business operations while ensuring reliability, scalability, and measurable customer outcomes.

Responsibilities

Architect AI Agents: Build and deploy multimodal AI agents that deliver measurable business impact for essential services organizations.
Collaborate with Customers: Work directly with enterprise stakeholders to understand business workflows, identify opportunities for automation, and implement solutions that improve revenue and operational efficiency.
Manage End-to-End Delivery: Own the complete implementation lifecycle, from design and development to deployment, performance measurement, and continuous improvement.
Ensure Scalability and Reliability: Develop dependable systems capable of supporting thousands of concurrent interactions while maintaining strong performance.
Conduct Rapid Experiments: Evaluate advanced text and voice models, fine-tune models, conduct live A/B tests, and iterate based on feedback and performance results.
Improve the Platform: Identify recurring customer needs, technical patterns, and opportunities to inform product development and engineering priorities.

Qualifications

Production Engineering Experience: At least 2 years of experience building and maintaining production systems using Python, including asyncio and FastAPI, and TypeScript, including Node.js and React.
AI Systems Expertise: Hands-on experience with large language models (LLMs), text-to-speech (TTS), speech APIs, model fine-tuning, vector databases, retrieval-augmented generation (RAG), and evaluation frameworks.
Performance Optimization: Ability to meet service-level agreements (SLAs), troubleshoot production issues, and optimize systems for speed, reliability, and performance at scale.
Monitoring and Infrastructure: Experience building production monitoring and debugging systems across cloud infrastructure and databases.
Customer-Focused Problem Solving: Comfortable working directly with customers to troubleshoot issues, understand edge cases, and deliver effective solutions quickly.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.

Head of Engineering

Company Overview

The company develops technology that helps organizations make enterprise data useful for AI applications. Its platform enables developers to build AI-powered systems that can understand, retrieve, and work with information from complex data sources, including enterprise documents.

The organization focuses on advancing document understanding and making it easier for businesses to build reliable AI applications. Its work spans data processing, retrieval, indexing, model evaluation, and the infrastructure required to deploy AI systems in production.

About the Role

The company is seeking a Head of Engineering to lead engineering execution, establish technical direction, oversee hiring, and develop a high-performing engineering organization.

Working closely with company leadership and product teams, the Head of Engineering will determine what to build, how to build it, and how to deliver reliable software to customers.

Responsibilities

Lead and develop the engineering organization by establishing clear priorities, ownership, accountability, and performance expectations.
Partner with company leadership to translate customer needs into a focused product and engineering roadmap and deliver software predictably.
Remain directly involved in technical execution through architecture reviews, code reviews, production debugging, and implementation of critical changes.
Set technical direction across application services, APIs, data pipelines, and machine learning infrastructure, balancing immediate customer requirements with long-term maintainability.
Collaborate with machine learning engineers and researchers on model training, fine-tuning, evaluation, and deployment to ensure model improvements translate into better production outcomes.
Guide model-serving decisions involving latency, throughput, GPU utilization, capacity planning, reliability, and inference costs.
Improve engineering practices for testing, observability, security, incident response, and software releases.
Recruit and develop strong engineers while coaching technical leaders and engineering managers.
Establish a culture of direct communication, customer focus, technical excellence, and accountability for results.

Qualifications

Experience leading multiple engineering teams in a B2B SaaS environment, at a scale comparable to a 20–30-person organization, with responsibility for delivering and operating customer-facing products.
Strong, current hands-on engineering skills, including the ability to write and review production code, evaluate architecture, and diagnose complex technical problems.
Practical familiarity with model serving and model training, including training or fine-tuning workflows, evaluation, deployment, and production operations.
Understanding of the tradeoffs among model quality, latency, throughput, GPU resources, reliability, and operating costs.
Strong background in backend systems, APIs, distributed systems, and cloud infrastructure.
Proven ability to recruit, mentor, and develop engineers, establish clear expectations, and provide constructive feedback.
Strong product judgment, including the ability to balance rapid delivery with correctness, reliability, and security.
Excellent written and verbal communication skills when working with technical teams, customers, and company leadership.

Preferred Qualifications

Experience scaling engineering organizations at a Series A–C startup.
Experience with document processing, optical character recognition (OCR), information extraction, retrieval, indexing, or other systems that work with enterprise data.
Experience with large language models (LLMs), multimodal models, evaluation datasets, and model quality monitoring.
Experience with GPU infrastructure, distributed training, inference optimization, or model-serving frameworks.
Experience building developer tools or API-first products.
Familiarity with enterprise requirements such as multi-tenancy, access controls, auditability, and security reviews.
Experience developing engineering managers while remaining closely involved in technical execution.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Machine Learning Director

Company Overview

The company is a mission-driven financial technology organization focused on expanding access to credit for underserved customers in emerging markets. As a Public Benefit Corporation, it develops technology that helps customers establish credit histories, achieve greater financial stability, and access financial opportunities.

Its offerings include point-of-sale financing and credit card products supported by patented secured-credit technology. By leveraging machine learning, data science, and AI-powered fraud prevention, the company has served more than 18 million customers as of 2025 while maintaining profitability and supporting sustainable growth.

About the Role

The company is seeking a Machine Learning Director to lead its ML Platform organization, overseeing a team of experienced Machine Learning Engineers and Software Engineers responsible for the end-to-end machine learning ecosystem.

The role encompasses machine learning infrastructure, including online and offline feature stores, model serving, ETL and feature architecture, and internal APIs that support customer-facing financial products.

Responsibilities

Define the technical roadmap for the ML platform, modeling infrastructure, and internal APIs, ensuring the long-term sustainability of data products, machine learning models, and offer-serving systems.
Lead, mentor, and manage Staff-level and Senior-level Machine Learning Engineers, fostering technical excellence in system design, scalability, and code quality.
Serve as the primary liaison between Data Science, ML Engineering, and business stakeholders across Risk, Fraud, Engineering, and Product.
Translate complex business objectives into actionable technical requirements and scalable engineering solutions.
Establish the long-term vision for machine learning infrastructure, including deployment, monitoring, and MLOps practices.
Ensure data products and machine learning systems deliver strong performance while remaining maintainable, secure, and aligned with applicable safety and compliance standards.
Support the professional development of direct reports by providing meaningful technical challenges, mentorship, and clear career progression opportunities.
Identify bottlenecks in global machine learning operations and implement systematic improvements across infrastructure and workflows.
Champion the adoption of emerging technologies, including large language models (LLMs) and graph databases, while maintaining platform stability and reliability.
Promote engineering best practices that improve operational efficiency, platform scalability, and the quality of production machine learning systems.

Qualifications

PhD or master’s degree in Computer Science, Statistics, Engineering, or a related field.
8+ years of hands-on experience in Data Science or Machine Learning Engineering.
At least 4+ years of leadership experience managing senior-level or Staff-level engineers.
Proven track record of designing and delivering large-scale machine learning systems.
Deep understanding of the full machine learning lifecycle, from feature extraction and data processing to model deployment and production monitoring.
Ability to evaluate system designs and architecture at a high level, identify technical risks, and guide engineering teams toward scalable solutions.
Experience designing and maintaining ML infrastructure, including feature stores, model-serving systems, data pipelines, and MLOps workflows.
Ability to identify bottlenecks in distributed or global machine learning operations and develop systematic, platform-level solutions.
Strong understanding of system design, software engineering practices, scalability, reliability, and maintainability.
Exceptional communication skills, including the ability to explain complex technical concepts to non-technical stakeholders.
Ability to defend technical decisions before executive leadership and advocate for an engineering-driven approach to problem-solving.
Strong leadership and mentoring skills, with the ability to develop senior technical talent and align engineering execution with business objectives.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Staff Machine Learning Engineer

Company Overview

The company is reimagining customer relationship management (CRM) by building an AI-powered world model of a business. By learning from emails, meetings, and customer conversations, its platform develops an evolving understanding of how organizations operate, enabling AI to anticipate needs, support better decisions, and take action.

Since launching its product in November, the company has served more than 5,000 businesses. It has raised over $70 million from prominent venture capital firms to transform the CRM software category. The founding team previously developed a product that reached more than 25 million users and brings experience from leading technology companies and AI organizations.

About the Role

The company is seeking a Staff Machine Learning Engineer to join its AI/ML team and build innovative AI experiences at the core of its product.

The team focuses on developing powerful, domain-specific AI systems that outperform general-purpose large language models in relevant business workflows. The role involves creating differentiated AI-powered products for sales teams and other business users, shaping the company’s AI/ML strategy, and transforming experimental concepts into reliable, production-ready features.

The Staff Machine Learning Engineer will collaborate closely with founders, executive leadership, and product leaders to identify high-value opportunities for AI, develop new solutions, and help build a world-class AI/ML engineering organization.

Responsibilities

Create and launch differentiated AI-powered experiences that provide meaningful value to sales teams and other business users.
Shape the company’s AI/ML strategy in close collaboration with founders and executive leadership.
Identify user needs that are well suited to AI/ML solutions, define the underlying problems, and collaborate with product leaders to develop effective solutions.
Prototype innovative LLM-powered experiences and guide their development into reliable, scalable product features.
Develop AI/ML capabilities that deliver specialized business intelligence and outperform generic language models in relevant use cases.
Contribute to technical decisions involving machine learning architectures, model selection, and AI product development.
Help establish and scale a high-performing AI/ML engineering team through recruiting, mentoring, and technical leadership.
Collaborate across engineering and product functions to ensure AI-powered features meet user needs and production requirements.

Qualifications

Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.
7+ years of experience building AI/ML-powered products, particularly in natural language processing (NLP) domains.
Strong understanding of deep learning frameworks, machine learning technologies, or cloud-based AI services.
Hands-on experience with MLOps, including the tools and processes required to deploy, monitor, and maintain machine learning systems.
Experience developing AI/ML solutions that address real-world product and business challenges.
Ability to translate complex user needs into practical AI-powered product capabilities.
Strong technical judgment and the ability to work collaboratively with founders, executives, and product leaders.

Preferred Qualifications

Experience leading AI/ML product initiatives from concept through production deployment.
Publications in scientific journals focused on applied AI or machine learning.
Familiarity with open-source and commercial solutions in the LLM operations ecosystem, including technologies such as LangChain, Llama, and Pinecone.
Experience building domain-specific AI systems and integrating large language models into production applications.
Experience mentoring engineers and contributing to the technical direction of an AI/ML team.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Head of AI Ads

Company Overview

The company provides a platform that helps brands improve their visibility and growth in the era of AI-powered search. As consumers increasingly rely on AI assistants and large language models to discover, evaluate, and purchase products and services, the company helps businesses become more visible, earn recommendations, and establish trust across emerging AI discovery channels.

Its platform supports brands in improving AI search visibility, optimizing content, and strengthening their presence across generative AI experiences. The company works with established businesses across financial technology, travel, enterprise software, and other industries. It is backed by prominent venture capital investors and operates across San Francisco, New York, and Montevideo.

About the Role

The company is seeking a Head of AI Ads to define, develop, and lead a new product line focused on advertising within AI-powered discovery platforms.

This role will establish the strategies and frameworks that help leading brands allocate advertising budgets across large language model-driven discovery channels. Combining performance marketing expertise, growth strategy, and early-stage product development, the position will transform emerging market opportunities into scalable products and services.

The successful candidate will help shape the future of digital advertising by developing new approaches to paid placements, organic authority, content optimization, and performance measurement across AI platforms.

Responsibilities

Category Blueprint and Strategy: Define the company’s approach to AI-native advertising by developing frameworks for budget allocation, paid placements, organic authority, and content optimization across large language model platforms.
Customer Growth Architecture: Partner directly with marketing executives and growth teams to develop early-adopter strategies, evaluate channel tradeoffs, design testing mechanisms, and measure full-funnel return on investment.
Experimentation and Innovation: Design and execute high-velocity quantitative experiments across emerging AI platforms to understand auction dynamics, recommendation algorithms, conversion triggers, and attribution models.
Paid and Organic Integration: Connect performance marketing with AI Engine Optimization (AEO), advising brands on when to use structured data, authority-building strategies, and direct paid placements.
Industry Thought Leadership: Represent the company publicly within the AI advertising ecosystem by publishing insights, educating executive buyers, and shaping industry perspectives on the future of digital discovery.
Product and Practice Development: Translate customer insights and experimental results into repeatable product specifications, scalable service offerings, and effective go-to-market strategies.

Qualifications

Performance Marketing Expertise: Demonstrated success leading large-scale performance marketing, search engine marketing (SEM), or paid media teams, with deep knowledge of auction mechanics, bidding strategies, targeting, and multi-touch attribution.
Relevant Experience: 6+ years of experience in performance marketing, growth, digital advertising, or related fields, preferably within high-growth startups.
First-Principles Innovation: A track record of pioneering new channels or markets before established playbooks existed, with the ability to navigate ambiguity and develop structured approaches from limited information.
Holistic Growth Strategy: Strong strategic judgment across paid and organic acquisition channels, with an understanding of how discovery mechanisms work together as an integrated ecosystem.
Quantitative Rigor: A data-driven approach to budget allocation, unit economics, incrementality testing, and performance measurement.
AI and Search Expertise: Strong interest in AI-powered discovery, the evolution of large language models, and answer-engine platforms such as ChatGPT, Perplexity, Gemini, and Claude.
Category-Building and Communication: Excellent communication skills, executive presence, and the ability to establish a new industry discipline at the intersection of AI, performance marketing, and brand growth.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
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Data Scientist

Company Overview

The company develops financial technology and intelligent financial infrastructure designed to help businesses manage spending, payments, risk, and accounting operations. Its platform automates financial workflows for more than 70,000 companies, managing over $200 billion in annualized spending.

About the Role

The company is seeking a Data Scientist to help advance its analytics capabilities by developing data products, generating actionable insights, and supporting continuous business improvement.

The role involves close collaboration with business stakeholders and cross-functional teams across Product, Engineering, Data, and Design to prioritize initiatives, improve reporting, measure performance, and drive operational improvements.

Responsibilities

Develop end-to-end data solutions, including models that consume, transform, and expose data to stakeholders and production systems.
Promote a culture of experimentation, rigorous testing, and analytical best practices to improve decision-making.
Contribute to the development of the data team’s processes, tools, and systems to support scalable and consistent business decisions.
Collaborate with Product, Engineering, Data, and Design teams to develop product roadmaps, establish priorities, and measure success.
Partner with data engineering teams to capture, move, store, and transform raw data into actionable insights.
Work closely with business teams to translate analytical findings into practical initiatives and measurable business outcomes.
Improve reporting capabilities and develop data products that help stakeholders identify opportunities and make informed decisions.
Support continuous process improvements through iterative technical solutions and data-driven analysis.

Qualifications

At least 4 years of professional experience working as a Data Scientist.
Strong SQL skills, including the ability to write efficient queries and experience with data warehouse technologies such as Amazon Redshift, Snowflake, or BigQuery.
Familiarity with business intelligence tools such as Looker, Omni, Sigma, Hex, or equivalent platforms.
Experience distributing analytical insights through reports, dashboards, and other data products.
A proven track record of delivering high-quality products and features at scale.
Ability to thrive in a fast-paced, continuously evolving environment that emphasizes iterative technical solutions and practical problem-solving.
Strong analytical thinking and the ability to collaborate with technical and nontechnical stakeholders.
Ability to translate complex data into clear recommendations that support business and product objectives.

Preferred Qualifications

Experience with the modern data stack, including Fivetran, Snowflake, dbt, Looker, Hightouch, or equivalent technologies.
Strong understanding of analytics engineering practices, including data modeling, version control, documentation, testing, and codebase development standards.
Experience in payments, financial technology, or related industries.
Familiarity with business-to-business enterprise sales metrics, processes, and performance analysis.

About the Data Teams

Product Data

The Product Data team develops data products and insights that influence product direction and create business value. The team also supports the platform used to launch, instrument, test, and quality-assure new products, working closely with Product, Engineering, and Design.

Risk & Capital Markets Data

The Risk Data team develops the analytical capabilities and infrastructure needed to evaluate risk and support growth across millions of businesses in the United States. Its focus areas include risk operations, underwriting, credit limits, financial product pricing, fraud detection, regulatory reporting, and capital market relationships.

Growth Data

The Growth Data team builds data products and insights that support customer acquisition, business expansion, and partnership growth. Its areas of focus include web analytics and marketing technology, outbound automation, customer data platforms, self-service products, go-to-market strategic finance, and business systems.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.