Senior AI/ML Engineer

General Motors•Sunnyvale, CA

1d•Hybrid

About The Position

This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA you are expected to report to that location three times a week. 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. You’ll 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.