Company Overview

A leading organization is seeking an experienced data engineering professional to help build and advance scalable, cloud-based data platforms that support enterprise analytics, operational reporting, and advanced data use cases. The organization’s data environment focuses on modern cloud technologies, enterprise data integration, governance, and reliable data delivery.

The organization is seeking a Lead Data Engineer to architect, build, and lead the development of scalable cloud-based data platforms. The role will provide technical leadership in designing and optimizing ETL/ELT frameworks using Azure data services, integrating information from ERP, CRM, operational systems, APIs, and third-party sources, and establishing robust data models within a modern Lakehouse architecture.

Key Responsibilities

• Lead the design, development, and optimization of scalable data pipelines supporting data ingestion, transformation, and enterprise-wide consumption.
• Architect and implement enterprise-grade ETL/ELT frameworks using Azure Fabric or comparable cloud data platforms.
• Oversee and optimize data integrations from ERP systems such as NetSuite and SAP, CRM platforms such as Salesforce, internal systems, APIs, and third-party data sources.
• Design and govern high-quality, scalable data models supporting analytics, reporting, operational systems, and advanced data use cases.
• Partner with Data Architects to define and implement Lakehouse patterns, Delta Lake strategies, medallion architecture, and domain-driven design principles.
• Establish and enforce data quality frameworks, validation standards, lineage tracking, and observability practices.
• Drive performance optimization, scalability, reliability, and cloud cost governance.
• Provide technical leadership and mentorship to data engineers.
• Conduct technical and architecture reviews while enforcing engineering best practices.
• Collaborate with analysts, application teams, architects, and business stakeholders to translate requirements into scalable data solutions.
• Lead Master Data Management (MDM), metadata management, governance, and data standardization initiatives.
• Oversee CI/CD automation, DevOps integration, testing frameworks, and monitoring strategies for data workflows.
• Evaluate emerging technologies and recommend platform improvements aligned with enterprise technology strategy.

Required Qualifications

• 10+ years of experience in data engineering, data architecture, or related roles.
• Proven experience leading large-scale data platform initiatives in cloud environments.
• Extensive hands-on experience with Azure data services, including Azure Data Lake, Data Factory, Fabric, Synapse, or similar technologies.
• Advanced proficiency in SQL and Python.
• Experience with Spark or other distributed data processing frameworks.
• Deep experience designing and implementing enterprise ETL/ELT frameworks.
• Strong expertise in data modeling, including dimensional modeling, star schema, and Lakehouse/Delta modeling.
• Experience integrating complex enterprise systems, including ERP, CRM, and operational platforms.
• Strong understanding of data governance, metadata management, MDM, and data quality frameworks.
• Experience with performance tuning, workload optimization, scalability, and cloud cost management.
• Demonstrated ability to lead technical teams, conduct architecture reviews, and mentor engineers.
• Strong problem-solving, debugging, and system design skills.
• Ability to collaborate effectively with technical and business stakeholders.
• Willingness to travel up to 5% depending on business needs.

Preferred Qualifications

• Experience with Delta Tables, Snowflake, Synapse, or comparable cloud data platforms.
• Experience with event-driven and streaming architectures, including Kafka, Event Hub, and streaming pipelines.
• Familiarity with finance, operations, energy, or ERP-driven data domains.
• Experience designing API-based data integrations and modern integration patterns.
• Azure certifications such as Data Engineer Associate, Solutions Architect, or equivalent.
• Experience enabling analytics teams, data science workflows, or machine learning pipelines.
• Experience implementing enterprise data security and compliance frameworks.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.

Company Overview

A technology company is building a data platform designed to support safe and fair decision-making. Its technology is used by more than 140,000 companies and millions of people for AI-powered verification across employment, housing, transportation, childcare, and other important areas.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI systems used by production engineering teams.
• Build production-grade ML services including model code, API layers, monitoring systems, and automated tests.
• Integrate LLM APIs such as OpenAI and Anthropic into production applications and workflows.
• Determine when to use LLMs, fine-tuned models, classical machine learning approaches, or rules based on cost, latency, quality, and business requirements.
• Write clean, maintainable, well-structured production software using strong object-oriented programming principles, appropriate abstractions, error handling, and testing practices.
• Develop and maintain APIs that support production ML and AI services.
• Work with CI/CD pipelines and ensure software is properly tested, reviewed, deployed, and maintained.
• Partner with Product Engineering, Product, and cross-functional teams to translate business requirements into practical ML solutions.
• Define API contracts and communicate technical approaches clearly to technical and non-technical stakeholders.
• Build evaluation frameworks and conduct experiments to measure and improve model and system performance.
• Make data-driven decisions and iterate quickly based on evaluation results and production performance.
• Develop AI-powered workflows that automate internal processes and improve engineering and operational efficiency.
• Build agentic workflows and contribute reusable skills and context to broader AI platforms.
• Contribute to the organization’s broader AI strategy and the development of scalable AI systems.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent professional experience.
• 6+ years of professional software engineering experience.
• At least 2 years of experience building and operating machine learning systems in production.
• Strong proficiency in Python with the ability to write clean, testable, well-structured code.
• Strong understanding of object-oriented programming and software engineering principles.
• Hands-on experience integrating LLM APIs into production systems.
• Experience with prompt engineering, structured outputs, function calling, cost management, and LLM evaluation.
• Experience building and maintaining APIs.
• Experience working with CI/CD pipelines and production software delivery.
• Experience using AI-assisted development tools, LLMs, code-generation tools, or agentic systems in production or operational environments.
• Ability to evaluate AI-generated code critically and ensure production software meets engineering quality standards.
• Strong problem-solving and communication skills.
• Ability to work effectively in fast-moving environments and take ownership of end-to-end deliverables.

Preferred Qualifications

• Experience with MLOps platforms such as MLflow, SageMaker, Vertex AI, or similar technologies.
• Background in document processing, OCR, or information extraction.
• Experience with PySpark or large-scale data processing.
• Experience with Ruby or Ruby on Rails.
• Familiarity with compliance-sensitive industries such as fintech, legal technology, or HR technology.
• Working knowledge of dbt, Snowflake, or modern ELT and data transformation tools.
• Experience building scalable AI agents and agentic workflows.
• Experience developing machine learning systems for high-volume production environments.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**

Company Overview

A forward-thinking technology organization is building a collaborative and innovative environment where employees can bring their authentic perspectives, passions, and experiences to work. Teams work closely with colleagues, cross-functional stakeholders, clients involved in digital transformation, and industry leaders shaping the future of digital manufacturing.

The organization promotes a self-starter culture where employees are encouraged to pursue their professional goals through leadership support, continuous learning, and development opportunities. Employees are provided with resources and dedicated time to grow their skills and reach their full potential throughout their careers.

The organization is seeking a Forward Deployed Engineer to work closely with internal teams, cross-functional stakeholders, and clients at the forefront of digital transformation. The role provides an opportunity to contribute to innovative digital manufacturing initiatives while collaborating with teams across the organization.

The successful candidate will thrive in a collaborative, self-directed environment and will have opportunities to develop professionally while working on challenging technology initiatives and engaging with clients and industry thought leaders.

Team Culture

• Collaborate closely with peers and cross-functional stakeholders across the organization.
• Work directly with clients involved in digital transformation initiatives.
• Engage with teams operating at the forefront of digital manufacturing thought leadership.
• Contribute perspectives, experiences, and ideas that strengthen team collaboration and innovation.
• Take ownership of professional goals within a supportive, self-starter culture.
• Access leadership guidance, learning opportunities, and professional development resources.
• Continue developing skills and capabilities as part of an ongoing career journey.

Benefits

• Competitive salary and stock options.
• Health care coverage.
• Life insurance.
• Health Savings Account (HSA).
• Flexible Spending Account (FSA), including coverage options for spouses and children.
• Flexible vacation policy.
• Adaptable working schedule and environment.

Perks

• Casual dress attire.
• Hybrid work flexibility.
• Catered lunches, snacks, and beverages.
• Commuter savings program.
• Company outings.
• Designated volunteering hours.
• Group volunteer events.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.

Company Overview

A technology company is building an Operations Cloud designed for physical product businesses to manage the flow of goods, dollars, and data in real time across procurement, inventory, orders, fulfillment, and finance.

The platform is designed to replace spreadsheet-heavy processes and rigid enterprise resource planning systems with a modern, AI-native solution. Its Adaptive Resource Platform and unified operational data model enable teams to deploy quickly, automate workflows, and adapt operations without lengthy re-implementation projects.

The organization has secured significant institutional investment to support the continued development of its platform and is focused on helping fast-growing businesses manage critical operations with greater speed, control, and visibility.

Key Responsibilities

• Own the applied AI architecture end-to-end, including model selection, orchestration, RAG over unified operational data, evaluations, guardrails, and production cost and latency management.
• Build an AI-powered operational interface that enables natural-language search, analysis, and automation using live supply-chain, finance, and operations data.
• Develop AI-assisted tooling that uses foundation models to propose, validate, and apply customer schemas and workflow definitions within production environments.
• Design agentic workflows that operate within enterprise security and operational guardrails.
• Ensure AI workflows are permission-aware, auditable, tenant-isolated, and safe to operate against customer systems of record.
• Build evaluation and observability infrastructure to measure system quality, identify regressions, and proactively detect anomalous or potentially risky behavior.
• Partner with product, design, and customer-facing engineering teams to identify and deliver high-impact AI use cases.
• Mentor engineers and help establish strong engineering practices for building, evaluating, and deploying LLM-powered systems.
• Establish scalable patterns for integrating foundation models with transactional systems and business-critical data.
• Make architectural decisions around AI capabilities based on practical business requirements, reliability, security, and operational considerations.

Required Qualifications

• Proven experience shipping LLM-powered features or AI agents to production and operating them at scale.
• Deep hands-on experience with foundation model APIs, RAG pipelines, tool use/function calling, prompt engineering, and context engineering.
• Experience with fine-tuning foundation models when appropriate for specific use cases.
• Experience building evaluation frameworks and guardrails for non-deterministic AI systems operating on business-critical data.
• Strong backend engineering fundamentals and experience designing reliable production systems.
• Experience working with PostgreSQL and cloud services for reliable, multi-tenant web applications.
• Strong understanding of AI system architecture, orchestration, data retrieval, evaluation, and production observability.
• Experience taking AI products or systems from initial concept to production in ambiguous or rapidly evolving environments.
• Strong judgment when determining where AI can provide value and where traditional software approaches are more appropriate.
• Ability to design secure and reliable AI systems capable of interacting with enterprise data and operational workflows.

Professional Qualities

• Team Player: Brings positivity, openness, and curiosity to collaborative engineering environments.
• Growth Mindset: Continuously looks for opportunities to learn and improve individual, team, and organizational capabilities.
• Craftsmanship: Maintains a strong focus on quality when designing and delivering software and AI systems.
• Customer First: Prioritizes the customer experience and works toward building useful, reliable, and engaging products.
• Pragmatic: Makes practical technical decisions based on the specific circumstances rather than relying solely on theoretical approaches.
• Low Ego: Works collaboratively and supports a team-oriented engineering culture.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**

Company Overview

A leading AI-powered performance marketing platform supports the mobile app economy by helping app marketers acquire and retain high-value users while enabling publishers to maximize revenue across programmatic and direct demand. Its technology solutions serve thousands of mobile businesses across more than 70 countries and span industries including gaming, social, finance, ecommerce, and entertainment.

The organization operates a global technology platform focused on machine learning, performance marketing, data intelligence, and scalable infrastructure. Its engineering teams work on large-scale systems that process significant volumes of machine learning inferences and support next-generation AI technologies.

The organization is building a next-generation Machine Learning Platform to support neural networks at significant scale and increase the intelligence and capabilities of its machine learning models.

The organization is seeking a Software Engineer, ML Data to join the Machine Learning Data Platform team and help design and build the infrastructure supporting large-scale machine learning workloads. The role will involve working with experienced ML, software, and infrastructure engineers to develop reliable, scalable, and cost-efficient systems for data lake infrastructure, dataset generation, model training, analytics, reporting, and monitoring.

The successful candidate will have strong software engineering and computer science fundamentals, with particular expertise in Python and Golang. Experience with machine learning platforms, large-scale data processing, Spark, and high-growth technology environments will be valuable.

Key Responsibilities

• Collaborate with experienced Machine Learning, Software, and Infrastructure Engineers to build and evolve the organization’s next-generation ML platform.
• Design, engineer, and implement reliable, scalable, and cost-efficient systems supporting machine learning workloads.
• Build and maintain data lake infrastructure for large-scale machine learning applications.
• Develop systems and tooling for dataset generation and management.
• Support model training infrastructure and enable next-generation machine learning models and technologies.
• Develop analytics, reporting, and monitoring infrastructure for machine learning systems.
• Leverage vendor-based technologies such as AWS and Weights & Biases to build scalable ML infrastructure.
• Utilize open-source technologies including PyTorch, PySpark, Trino, Hive, Iceberg, and ClickHouse.
• Work with internal tooling and infrastructure to support machine learning data workflows.
• Help improve the scalability, reliability, efficiency, and cost-effectiveness of ML data systems.
• Deliver business results by enabling the development and deployment of increasingly capable machine learning models.
• Balance engineering quality and technical excellence with the practical need to deliver solutions efficiently.

Required Qualifications

• 6+ years of professional industry experience.
• Very strong programming skills in Python and Golang.
• Strong computer science fundamentals, including data structures, algorithms, and system architecture.
• Experience designing and building reliable and scalable software systems.
• Strong understanding of distributed systems and large-scale infrastructure.
• Ability to design and implement systems that prioritize reliability, scalability, and cost efficiency.
• Strong problem-solving and software engineering skills.
• Passion for quality and technical excellence while maintaining a practical approach to delivering projects.

Preferred Qualifications

• Experience working with Apache Spark or similar large-scale data processing technologies.
• Experience in a high-growth startup or rapidly evolving technology environment.
• Experience building machine learning tooling, platforms, or infrastructure.
• Experience applying machine learning infrastructure to large-scale problems.
• Familiarity with technologies such as AWS, Weights & Biases, PyTorch, PySpark, Trino, Hive, Iceberg, and ClickHouse.
• Experience working with data lakes, dataset generation, model training infrastructure, analytics, reporting, or monitoring systems.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.

Company Overview

A mission-driven organization focused on improving access to early childhood education through technology, personalized coaching, and responsive support. The organization works with childcare providers and small business owners to help them launch and grow in-home programs while also partnering with government organizations to modernize public childcare infrastructure.

Key Responsibilities

• Work directly with users and internal teams to identify high-impact problems and develop product solutions.
• Write production code and deliver frequent improvements across the customer journey.
• Use AI-powered development tools such as Windsurf, Cursor, and similar technologies to improve development velocity and output quality.
• Build and deploy AI-powered tools that reduce manual effort and improve experiences for providers, parents, and government partners.
• Automate onboarding workflows, proposal generation, reporting processes, and other operational activities.
• Own the complete product development cycle, including user discovery, solution design, development, testing, deployment, monitoring, and adoption.
• Prioritize initiatives based on business impact and collaborate across teams to deliver measurable results.
• Work closely with sales, onboarding, and account management teams to automate workflows and improve customer experiences.
• Identify opportunities to use AI and automation to unlock new growth and operational efficiencies.
• Travel as needed to work directly with customers and understand real-world operational challenges.
• Continuously gather customer feedback and use insights to improve products and workflows.

Success in the Role

• Identify user pain points quickly and consistently deliver solutions that address them.
• Develop and deploy AI-powered internal tools and product experiences that improve key business metrics such as conversion, retention, and revenue.
• Own feedback loops from initial discovery through deployment and continued iteration.
• Enable internal teams to operate more efficiently with fewer manual processes and resources.
• Maintain close relationships with customers while moving quickly from problem identification to implementation.
• Operate independently with a high level of ownership and minimal supervision.

Required Qualifications

• Experience building products, launching initiatives, acquiring users, or independently generating measurable business results.
• Strong technical ability with experience writing and shipping production code.
• Experience with Python, JavaScript, or similar programming languages.
• Hands-on experience using AI and large language models (LLMs).
• Familiarity with AI-powered developer tools such as Windsurf and Cursor.
• Ability to communicate directly with users and translate customer insights into product and technical decisions.
• Strong understanding of business priorities and the ability to prioritize initiatives based on return on investment (ROI).
• Experience working across engineering, product, operations, sales, or other cross-functional teams.
• Strong problem-solving and analytical skills.
• Ability to operate effectively in ambiguous, fast-paced environments.
• High level of ownership and ability to work independently.

Preferred Experience

• Experience founding or helping build a company.
• Experience launching products that gained users or generated revenue.
• Consulting experience or exposure to business strategy.
• Experience managing a P&L or other business ownership responsibilities.
• Business school experience.
• Experience developing AI-powered applications and internal tools.
• Experience working directly with customers in a forward-deployed engineering, solutions engineering, consulting, or similar capacity.

Why Join

• Contribute to technology that supports families, childcare providers, small business owners, and communities.
• Take ownership of strategically important initiatives and operate with a founder-like mindset.
• Work with a small, fast-moving team focused on rapid execution.
• Engage directly with users and see products go live quickly.
• Build AI-powered solutions that address real-world operational challenges.
• Help shape the future of early childhood education and childcare through technology.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.

Company Overview

A leading technology company focused on trust and safety helps businesses create safer and more trusted digital experiences. Its technology enables organizations to detect fraud, prevent online abuse, protect users, and create frictionless experiences for legitimate consumers.

Key Responsibilities

• Work with Trust and Safety Architects and Data Science teams to identify emerging fraud patterns across the network and proactively address threats.
• Detect behavioral and transactional patterns and translate findings into stronger signals, improved configurations, and more effective decisioning logic.
• Work across different industry verticals and collaborate closely with customers, partners, and prospects with varying risk priorities.
• Develop dashboards, tune and build models, decision logic, and custom signals to help customers achieve their desired business outcomes.
• Identify sources of false positives, coverage gaps, and other vulnerabilities by analyzing raw event streams.
• Form hypotheses, design tests, analyze results, and implement solutions to address identified vulnerabilities.
• Lead forensic investigations during fraud spikes by tracing attack patterns to their source and identifying techniques used by attackers.
• Produce clear investigation reports with actionable remediation recommendations.
• Distinguish between isolated anomalies and systemic gaps that may represent opportunities for product improvements.
• Contribute to fraud detection frameworks, investigative tools, and internal playbooks.
• Share field observations and customer insights to help inform and accelerate future product development.
• Collaborate with technical and non-technical stakeholders to communicate findings and recommended actions.
• Travel periodically as required by the role.

Required Qualifications

• 5–8 years of experience in fraud, trust and safety, risk, or a closely related data science field.
• Significant experience working directly with fraud-related data.
• Strong SQL and Python skills with the ability to use code to investigate and answer complex questions.
• Strong understanding of machine learning concepts applied to fraud detection, including classification models, feature engineering, precision/recall tradeoffs, threshold calibration, and score drift.
• Experience analyzing large-scale behavioral or transactional datasets to identify patterns, anomalies, and potential fraud activity.
• Ability to recognize fraud patterns and investigate complex behavioral signals using data.
• Strong written and verbal communication skills with the ability to explain technical findings to both technical and non-technical stakeholders.
• Ability to produce detailed forensic investigation reports and present findings to senior risk and business leaders.
• Customer-facing experience and an understanding of how business priorities can vary across different organizations.
• Strong problem-solving skills and the ability to balance analytical findings with customer-specific objectives.

Preferred Qualifications

• Hands-on experience with fraud detection platforms, whether internally developed or third-party solutions.
• Experience building AI-powered solutions using LLM APIs, prompt engineering, or agentic workflows.
• Experience applying AI to automate investigation processes, identify patterns in raw data, or accelerate fraud analysis.
• Familiarity with real-time event processing systems.
• Experience working with rules-based decisioning systems alongside machine learning models.
• Background in payments, e-commerce, fintech, marketplaces, or account security fraud.
• Previous experience in forward-deployed engineering, staff engineering, or embedded consulting at a technical product company.
• Degree in Computer Science, Data Science, Mathematics, Statistics, Information Systems, Economics, or a related field, or equivalent practical experience.

Compensation

The anticipated starting on-target earnings range for this role is $170K – $230K. The position may also be eligible for equity and other benefits. Compensation may vary based on factors including level, location, transferable skills, work experience, business needs, and market conditions. The compensation range and final offer may be adjusted in the future.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**

Company Overview

A growing healthcare technology company develops software solutions that help healthcare organizations manage complex operational and financial processes. Its engineering teams work closely with customers and internal stakeholders to build technology that addresses real-world challenges while continuously improving the core product.

The organization is seeking a Forward Deployed Software Engineer to work at the intersection of technology, business strategy, and customer outcomes. This role will partner directly with leadership, customers, and product teams to solve complex technical challenges and develop long-term software solutions that integrate effectively with customer environments.

The successful candidate will combine strong software engineering capabilities with customer-facing skills, business understanding, and an ability to translate complex technical requirements into practical solutions. The role involves designing, building, and integrating software while contributing to the broader product strategy and improving solutions across a growing customer base.

Key Responsibilities

Design and Implement Solutions

• Design and architect technical solutions within the core product that align with customer requirements.
• Leverage AI and machine learning models and techniques to automate solutions in innovative ways.
• Fix bugs, implement features, and resolve critical technical problems across the platform.
• Develop software solutions that integrate effectively with customer technical environments.
• Build scalable solutions that address immediate customer needs while supporting long-term product development.

Partner With Customers and Operations

• Build strong relationships with subject-matter experts, users, managers, and executives who rely on the platform.
• Work directly with customers to understand technical requirements and develop effective solutions.
• Serve as a technical expert for the organization’s products and services.
• Clearly communicate technical concepts and solutions to both technical and non-technical audiences.
• Address customer questions and concerns while building trust through effective technical collaboration.

Drive Organizational Impact

• Collaborate with cross-functional teams, including sales, product management, and product engineering, to ensure successful solution implementation.
• Provide product feedback based on customer requirements, market trends, and real-world usage.
• Develop solutions that improve the overall product and create value across the broader customer base.
• Partner with product and engineering teams to ensure customer-driven solutions contribute to the long-term product vision.
• Identify opportunities to improve existing systems and develop scalable approaches to recurring customer challenges.

Required Qualifications

• 5+ years of hands-on software engineering experience.
• Strong software development and problem-solving skills.
• Experience designing and implementing production software solutions.
• Ability to solve complex, real-world technical problems with a strong focus on customer outcomes.
• Experience collaborating effectively with technical and non-technical stakeholders.
• Strong communication and interpersonal skills.
• Ability to translate customer requirements into practical technical solutions.
• Experience working in fast-paced environments with changing priorities.
• Ability to work independently, identify blockers, and take ownership of technical challenges.
• Strong interest in applying AI/ML technologies to solve novel business and technical problems.

Preferred Attributes

• Customer-focused approach with strong empathy for users and stakeholders.
• Entrepreneurial mindset and willingness to take initiative.
• Growth mindset and enthusiasm for continuous learning.
• Ability to build software iteratively alongside subject-matter experts.
• Strong collaboration and influencing skills.
• Ability to balance immediate customer needs with long-term product strategy.
• High standards for software quality, reliability, and delivery.
• Ability to move quickly while maintaining a high quality bar.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**

Company Overview

A healthcare technology company is focused on improving access to care by bringing healthcare services directly into patients’ homes. Its technology-enabled care model is designed to help patients receive preventive and chronic care while reducing barriers associated with traditional healthcare delivery.

Key Responsibilities

Actuarial & Economic Modeling

• Build total-cost-of-care, PMPM, and MLR models using administrative claims data.
• Quantify the long-term financial impact of healthcare programs and interventions.
• Project changes in healthcare costs, utilization, and risk over multi-year horizons.
• Quantify and communicate uncertainty associated with economic projections.
• Produce model outputs and tables that payer actuarial teams can incorporate into pricing, reserving, and bid work.
• Develop defensible methodologies for evaluating the economic value of healthcare interventions.

Payer Credibility & Commercial Support

• Represent the organization in MLR and medical-economics discussions with health plans.
• Engage directly with payer actuaries and medical-economics teams to explain methodologies and findings.
• Translate quantitative analysis into value narratives that quality, risk, and finance teams can use.
• Support commercial teams in pricing and communicating the economic impact of healthcare programs.
• Develop analyses that can support customer and partner decision-making.

Measurement & Analytical Rigor

• Define appropriate methodologies for determining whether interventions caused measurable changes in healthcare costs and outcomes.
• Partner with Data Science teams on causal inference, experimental design, and measurement strategies.
• Clearly distinguish between value that can be demonstrated through available evidence and value that remains an assumption or projection.
• Apply rigorous analytical methods to ensure models and conclusions are defensible.
• Continuously improve modeling approaches based on new data, evidence, and business requirements.

Required Qualifications

• Deep experience building actuarial or health-economic models using administrative claims data.
• Experience with total cost of care, PMPM, healthcare utilization, trend analysis, and risk modeling.
• Strong understanding of methodologies used by health payers, including MLR, risk adjustment, and multi-year projections.
• Strong judgment regarding the appropriate application and limitations of actuarial and health-economic methodologies.
• Strong SQL skills.
• Proficiency in Python or R.
• Ability to independently build, maintain, and own analytical models end to end.
• Ability to communicate effectively with actuaries, medical-economics teams, and other non-technical stakeholders.
• Strong understanding of causal inference and the ability to communicate what an analytical design can and cannot demonstrate.
• Ability to translate complex quantitative analysis into clear business and economic insights.

Preferred Qualifications

• Actuarial credentials such as ASA, FSA, or MAAA, or demonstrated progress toward actuarial examinations.
• Experience working with health payers, value-based care organizations, or risk-bearing entities.
• Familiarity with Medicare Advantage, Stars, and risk adjustment.
• Experience producing analyses that customers or partners have incorporated into pricing or reserving processes.
• Experience using AI coding assistants such as Claude Code or Cursor as part of a development workflow.

Work Environment

The organization operates on a hybrid schedule, with employees working from the office Monday through Thursday and working from anywhere on Fridays. The workplace emphasizes collaboration while maintaining flexibility and work-life balance.

Employees have access to provided lunch each day, with the team sharing an hour together as an opportunity to connect outside of regular meetings.

Disclaimer

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

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

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**

Company Overview

A technology company is developing a new generation of computing experiences designed to make computers feel more natural and human. Its work focuses on voice agents and advanced AI technologies, bringing together experienced leaders and technical experts across hardware and software to develop systems capable of seeing, hearing, and collaborating with people in natural ways.

The Data & Analytics organization supports the development of AI systems through sophisticated data infrastructure and engineering capabilities. Its data includes conversations, voice recordings, sensor signals, product telemetry, and other complex multimodal sources.

The organization is seeking a Data Engineer, Machine Learning to build and maintain the data pipelines that support AI and machine learning models. This role will work closely with machine learning engineers and researchers to ensure they have the right data, in the right format, and at the right time to train, evaluate, and deploy models.

Key Responsibilities

• Design and build production data pipelines that prepare conversational, voice, and multimodal data for model training and evaluation.
• Partner directly with machine learning engineers to understand data requirements for new models and experiments.
• Deliver high-quality datasets that meet the requirements of machine learning workflows.
• Build and maintain infrastructure for dataset versioning, lineage tracking, and reproducibility.
• Ensure training runs can be traced back to their specific source datasets and data versions.
• Develop data quality frameworks that identify potential issues before they impact model quality.
• Implement schema validation, data drift detection, and coverage monitoring.
• Optimize large-scale data processing for cost and performance across cloud infrastructure.
• Build internal tools that enable ML engineers and researchers to independently discover, explore, and request data.
• Define and enforce data governance and privacy standards, particularly for sensitive conversational and voice data.
• Contribute to architectural decisions for the broader data platform as data volume and organizational requirements grow.
• Support the complete machine learning data lifecycle, from data collection and labeling through training and evaluation.
• Collaborate closely with ML engineers and researchers to improve data workflows and accelerate model development.

Required Qualifications

• 5+ years of experience in data engineering, including meaningful experience supporting machine learning or AI teams.
• Strong SQL and Python skills with the ability to use both regularly in production environments.
• Experience building and operating ETL/ELT pipelines at scale using modern data platforms and tooling.
• Experience with workflow orchestration systems such as Airflow, Dagster, or Prefect.
• Hands-on experience with machine learning data workflows, including training data pipelines, dataset versioning, data labeling pipelines, or model evaluation data.
• Strong understanding of machine learning workflows and the characteristics of high-quality training datasets.
• Understanding of how data quality can directly affect machine learning model performance.
• Experience working with unstructured and semi-structured data, including audio, text, and JSON logs.
• Strong communication skills and the ability to bridge data infrastructure requirements with machine learning needs.
• Ability to collaborate effectively with machine learning engineers, researchers, and other technical teams.

Preferred Qualifications

• Experience with vector databases, embedding storage, or feature stores.
• Experience working with hardware or embedded-system data, including telemetry, sensor data, or real-time streams.
• Experience with distributed computing frameworks such as Ray or Spark.
• Experience with Kubernetes and managed Kubernetes environments such as GKE or EKS.
• Knowledge of data privacy frameworks, particularly those applicable to voice and conversational data.
• Experience building internal developer tools or self-service data platforms.
• Experience designing infrastructure that supports scalable machine learning data workflows.
• Familiarity with data lineage, reproducibility, and governance practices for AI systems.

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