Machine Learning

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Company Overview

A leading financial technology organization is building and operating a Machine Learning Platform that provides the infrastructure, tooling, and developer experience required to support machine learning across the company. The platform enables data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently.

The organization focuses on building scalable and reliable technology that enables ML teams to move quickly while maintaining strong standards for governance, performance, reliability, and cost efficiency.

San Francisco

The Machine Learning Platform team is seeking a Senior Software Engineer, Machine Learning Platform to design and build scalable systems supporting model training, feature computation, real-time inference, and experimentation.

This role operates at the intersection of distributed systems, cloud infrastructure, and applied machine learning. The successful candidate will help establish robust technical foundations that allow machine learning teams to efficiently develop and operate production systems.

The base salary offered for this role and level of experience will begin at $187,000.00 and goes up to $259,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on location, skills, qualifications, and experience.

Key Responsibilities

• Design, build, and operate scalable machine learning infrastructure on AWS.
• Develop distributed training and batch processing systems using Ray.
• Build and maintain infrastructure-as-code using Terraform.
• Support and evolve feature stores and feature pipelines.
• Develop data ingestion and streaming systems using technologies such as Kinesis, Kafka, Flink, Spark, or similar platforms.
• Improve CI/CD workflows for machine learning models and platform components.
• Enhance observability, reliability, and cost visibility across machine learning workloads.
• Partner closely with Data Science and ML Engineering teams to improve developer experience.
• Contribute to platform architecture decisions and technical roadmaps.
• Participate in on-call rotations to support production systems.

Required Qualifications

• 5+ years of experience in ML infrastructure, platform engineering, or production ML systems.
• Knowledge of the machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Experience with distributed systems, cloud computing, or large-scale data processing.
• Strong foundation in computer science and software engineering principles.
• Strong interest in the impact and evolution of advanced AI technologies.
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code.
• Experience with containerization technologies such as Docker and Kubernetes and orchestration systems.
• Knowledge of cloud platforms such as AWS and distributed computing frameworks such as Spark and Ray.
• Experience with GPU programming, including CUDA, as well as GPU cost management and optimization.
• Strong programming skills in Python, Go, Scala, Java, or similar languages.
• Familiarity with infrastructure-as-code technologies such as Terraform and CloudFormation.
• Solid understanding of software engineering fundamentals, including testing, version control, code review, and observability.

Nice-to-Have Qualifications

• Experience with distributed compute frameworks such as Ray.
• Experience building or operating a feature store.
• Experience with real-time machine learning systems or model serving.
• Familiarity with streaming technologies such as Kafka, Kinesis, Flink, and Spark Streaming.
• Experience supporting machine learning lifecycle workflows, including training, evaluation, deployment, and monitoring.
• Knowledge of ML experimentation platforms and model governance practices.

Disclaimer

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