Senior ML Systems Engineer
About The Position
The Data Labeling Engineering team designs, builds, and operates hybrid human- and machine-powered data-labeling tools and pipelines that provide training data for autonomous-vehicle machine-learning models within General Motors' AV organization. The team works across software engineering, data engineering, and AI/ML to define strategies, tools, and quality controls for reliable training data at scale. Its platform serves thousands of users and consumers and includes TypeScript/React, Python, GraphQL, Go, and ML model services. The Senior ML Systems Engineer builds and operates scalable labeling experiences, services, and automation that help teams label data accurately and efficiently. The role integrates machine-learning-driven annotation, including pre-labeling, autolabeling, and active-learning loops, and supports ML teams with workflow and data-quality insights. The engineer owns projects from problem framing through rollout, contributes to design and code reviews, and collaborates with ML, Operations, Product, Data Science, and platform teams. The role is hybrid, with at least three days per week at a designated location or another frequency set by the manager, and requires less than 25% travel.
Requirements
- At least 6 years of experience building robust distributed platforms and applications.
- Hands-on experience using AI tools, such as agentic coding, search, and documentation generators, to accelerate understanding, implementation, debugging, and delivery.
- Proficiency writing and reviewing high-quality, scalable, performant full-stack code using technologies such as Python, TypeScript, Go, React, SQL, Redux, GraphQL, or WebGL.
- Solid understanding of relational databases, data modeling, and API design.
- Strong fundamentals in object-oriented design, design patterns, data structures, algorithms, and engineering best practices, including test-driven development, code quality, observability, and CI/CD.
- Experience developing and operating cloud-based applications.
- Experience shipping and operating end-to-end products or features in production.
- Strong communication and collaboration skills, including explaining tradeoffs, influencing peers, and working through ambiguity with cross-functional partners.
- Empathy for user challenges and an interest in making complex workflows intuitive.
- Passion for self-driving technology and its potential to transform safety, mobility, and the driving experience.
- Willingness to learn new technologies and deepen expertise across frontend, backend, and data- or ML-adjacent systems.
Nice To Haves
- Experience using modern web APIs such as Service Workers, Cache Storage, and IndexedDB 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 those involving labeled data, data quality, or autolabeling loops.
- Familiarity with data-labeling platforms or tools used by large labeling workforces, such as annotation interfaces, workflow engines, and quality systems.
- Experience with A/B testing and telemetry or observability systems to measure impact and reliability.
- Proficiency writing and reviewing high-quality, scalable, and performant code using TypeScript, React, Redux, GraphQL, WebGL, or similar frontend technologies.
Responsibilities
- Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies.
- Ship features across multiple product areas that improve the speed and accuracy of labeling data for new models and cities.
- Develop automation and tooling, such as efficiency dashboards, automated quality assurance, and autolabel review tools, to provide ML engineers with insight into labeling workflows and data quality.
- Collaborate with ML engineers to design and integrate machine-learning-driven data annotation, including pre-labeling, autolabeling, and active-learning loops.
- Use and advocate for AI-powered development workflows, including code assistants, automated documentation, and test generation.
- Own technical projects from problem framing through design, implementation, and rollout.
- Drive code reviews, design discussions, and technical decisions.
- Collaborate with ML, Operations, Product, Data Science, and platform teams to translate requirements into workflows, APIs, and user interfaces that meet quality, cost, and latency goals.
Benefits
- Incentive pay program with payouts based on company performance, job level, and individual performance.
- Medical benefits.
- Dental insurance.
- Vision insurance.
- Health Savings Account.
- Flexible Spending Accounts.
- Retirement savings plan.
- Sickness and accident benefits.
- Life insurance.
- Paid vacation.
- Paid holidays.
- Relocation benefits for candidates who qualify under company policy.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
