Ofertas › General Motors
General Motors · Flexible / Remote; Sunnyvale, CA · Remoto
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 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. What You'll Do 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 technolo