AI/ML Engineer

DescriptionHelp 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. 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 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 re