Senior AI​/ML Engineer

Location

Remote/Hybrid:
This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA you are expected to report to that location three times a week.

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  tools and platform are used by thousands of users and consumers.

We own a modern full‑stack architecture including Type Script/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.

• Build high‑impact labeling experiences by designing, implementing, and testing scalable, high‑performance user experiences and services using modern full‑stack and/or frontend technologies.
• Ship features across 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 by developing automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto‑QA, autolabel review tools) to reduce iteration time from idea to trained model.
• Apply ML to labeling itself by collaborating 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 by using and advocating for modern AI‑powered development workflows (code assistants, automated documentation, test generation, etc.) to increase velocity while maintaining quality.
• Own projects end‑to‑end: take ownership from problem framing through design, implementation, and rollout; drive code reviews, design discussions, and technical decisions.
• Collaborate across the AV stack by working 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.

• 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.

• 6+ years of experience building robust distributed platforms and applications.
• Hands‑on experience leveraging AI tools (agentic coding, search, documentation generators, 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…