Staff ML Systems Engineer

General Motors•Washington, DC

1d•$171,700 - $303,900•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 looking for end-to-end ownership of meaningful pieces of the platform, growth in technical and strategic leadership, and direct impact across teams and systems that unblock the next generation of AV capabilities.

Requirements

8+ years of experience building robust distributed platforms and applications.
Hands-on experience leveraging AI tools (agentic workflows, knowledge acquisition, documentation generation, operational triage, 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, gRPC, GraphQL, WebGL, etc.
Solid understanding of scalable software system design including data modeling and API/interface design.
Strong fundamentals in object‑oriented design and design patterns, data structures, algorithms, and engineering best practices (TDD, code quality, observability, CI/CD).

Nice To Haves

A track record of close collaboration with customers, product managers, designers, and/or user experience researchers.
Experience with computer vision, machine learning, or data‑centric AI projects — especially where data annotation, data quality, or autolabeling loops were central to the work.
Familiarity with data labeling/annotation 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.
Experience developing data‑intensive or visualization‑heavy applications.

Responsibilities

Define the platform vision and roadmap
Work with cross-team and cross-functional leads to understand current and future needs, translating their input into an aligned platform vision and an incremental development roadmap.
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 Engineering, Operations, Product, Data Science, other platform teams) to translate abstract requirements into concrete workflows, APIs, and UIs that hit quality, cost, and latency goals.
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.
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 help the team ship features spanning multiple surface-areas that directly affect how quickly and accurately we can label data for new models and cities.
Champion AI‑assisted engineering
Use and advocate for modern AI‑powered development workflows (code assistants, automated documentation, test generation, etc.) to increase build-velocity while maintaining code and product quality.