Senior ML Systems Engineer

General Motors • Sunnyvale, CA

1d • $174,900 - $261,300 • Hybrid

About The Position

Help teach our self-driving vehicles how to see and understand the world! The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV organization. We operate in the intersection of software engineering, data engineering, and AI/ML, defining the strategies, tooling, and quality controls that create reliable training data at scale. Our tools and platform are used by thousands of users and consumers. We own a modern full-stack architecture including TypeScript/React, Python, GraphQL, Golang, and ML model services, which powers data-annotation pipelines and machine-led training data solutions at foundation-model scale. We partner closely across AI/ML engineers, Product Operations, Product Management, Data Science, and other ML Platform groups. This role is ideal for an engineer who wants end-to-end ownership of meaningful pieces of the platform, growth toward technical leadership, and direct impact on systems that unblock the next generation of AV capabilities.

Requirements

• 6+ years of experience building robust distributed platforms and applications.
• Hands-on experience leveraging AI tools (agentic coding, search, documentation generators, etc) to accelerate understanding, implementation, debugging, and delivery of new capabilities.
• Proficiency in writing and reviewing high-quality, scalable, and performant full-stack code using technologies and languages like Python, TypeScript, Go, React, SQL, Redux, GraphQL, WebGL.
• Solid understanding of relational databases, data modeling, and API design.
• Strong fundamentals in object-oriented design and design patterns, data structures, algorithms, and engineering best practices (TDD, code quality, observability, CI/CD).
• Experience developing and operating cloud-based applications.

Nice To Haves

• Experience using modern web APIs (Service Workers, Cache Storage, IndexedDB, etc.) in data-intensive or visualization-heavy applications.
• A track record of close collaboration with customers, product managers, designers, and user experience researchers.
• Experience with computer vision, machine learning, or data-centric AI projects — especially where labeled data, data quality, or autolabeling loops were central to the work.
• Familiarity with data labeling platforms or tools used by large labeling workforces (e.g., annotation UIs, workflow engines, quality systems).
• Experience with A/B testing and telemetry/observability systems to measure impact and reliability.
• Proficiency in writing and reviewing high-quality, scalable, and performant code using TypeScript, React, Redux, GraphQL, WebGL, or similar frontend technologies.

Responsibilities

• Build high-impact labeling experiences: Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies. Ship features spanning multiple surface-areas that directly affect how quickly and accurately we can label data for new models and cities.
• Level up how ML teams work with data: Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto-QA, autolabel review tools), reducing iteration time from idea to trained model.
• Apply ML to labeling itself: Collaborate with ML engineers to design and integrate ML-driven data annotation (pre-labeling, autolabeling, active learning loops), helping us move from human-only to machine-led labeling at scale.
• Champion AI-assisted engineering: Use and advocate for modern AI-powered development workflows (code assistants, automated documentation, test generation, etc.) to increase velocity while maintaining quality.
• Own projects end-to-end: Take ownership of technical projects from problem framing through design, implementation, and rollout. Drive code reviews, design discussions, and technical decisions.
• Collaborate across the AV stack: Work with partner teams (ML, Ops, Product, Data Science, other platform teams) to translate abstract requirements into concrete workflows, APIs, and UIs that hit quality, cost, and latency goals.

Benefits

• medical
• dental
• vision
• Health Savings Account
• Flexible Spending Accounts
• retirement savings plan
• sickness and accident benefits
• life insurance
• paid vacation & holidays
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
• relocation benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed