Senior ML Systems Engineer

Job Description 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.

What You’ll Do

• 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

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