ML Systems Engineer

General Motors•Washington, DC

1d•$90,100 - $191,800•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 high‑quality hybrid human/machine labeling tools and pipelines that power autonomous vehicle machine learning models across General Motors. We sit at the intersection of software engineering, data engineering, and ML, defining labeling strategies, tooling, and quality controls that create reliable training data at scale. Our team builds the mission‑critical products that let people and machines add “ground truth” labels to roads, objects, and complex driving scenarios in sensor data from self‑driving cars. These tools are used by thousands of labelers and dozens of ML teams to train and evaluate the models behind advanced driver assistance and autonomous features. We own a modern full‑stack architecture including TypeScript, React, GraphQL, Python, Golang, and ML model services, leveraging cloud platforms and workflow orchestration tools (e.g., Airflow) to power data‑annotation pipelines and ML‑led labeling solutions at foundation‑model scale. We partner closely with ML engineers, Operations, Product Management, Data Science, and other ML Platform groups. This role is ideal for an engineer who wants to own meaningful pieces of the stack, grow toward technical leadership, and work directly on systems that unblock the next generation of AV models.

Requirements

Passionate about self‑driving technology and its potential to transform safety, mobility, and the driving experience.
Driven to learn new technologies and deepen your expertise across frontend, backend, and data/ML‑adjacent systems.
Proven experience shipping and operating end‑to‑end products or features in production.
Strong communication and collaboration skills; you can explain tradeoffs, influence peers, and work through ambiguity with cross‑functional partners.
Empathetic to user challenges (from labelers to ML engineers to Ops) and excited to turn messy workflows into simple, intuitive tools.
2+ years of experience building robust web applications.
Bachelors degree or higher in Computer Science or related field. Or relevant work experience.
Hands‑on experience leveraging AI tools (agentic coding, search, documentation generators) to accelerate understanding, implementation, and delivery of customer value.
Proficiency in writing high‑quality, scalable, and performant code using TypeScript, React, Redux, GraphQL, WebGL, or similar frontend technologies.
Solid understanding of relational databases, data modeling, and API design.
Strong fundamentals in object‑oriented design, data structures, algorithms, and engineering best practices (testing, code review, observability, CI/CD).
Experience developing and operating cloud‑based applications.
Experience with A/B testing and telemetry/observability systems to measure impact and reliability.

Nice To Haves

4+ years of experience in the software.
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).

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 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‑in‑the‑loop labeling at scale.
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.
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.