ML Systems Engineer, Data Labeling Engineering - Early Career

General Motors•Sunnyvale, CA

2d•$125,000 - $165,000•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 hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors’ AV organization. We work at 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. These systems power data-annotation pipelines and machine-led training data solutions at foundation-model scale. We partner closely with AI/ML engineers, Product Operations, Product Management, Data Science, and other ML Platform groups. As an early-career Software Engineer on the Data Labeling Engineering team, you will build tools and services that help machine learning teams create high-quality training data for autonomous driving. Your work may span frontend experiences, backend services, data pipelines, machine learning integrations, and quality systems used by labelers, ML engineers, and operations teams. This role is designed for a recent college graduate or engineer early in their career who wants to own meaningful pieces of a platform, grow their technical expertise, and work directly on systems that enable the next generation of AV capabilities. You will learn from experienced engineers while contributing to production systems and developing depth across frontend, backend, data, and ML-adjacent technologies.

Requirements

Recently completed a bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related STEM field. For completed degrees, graduation must have occurred within the past 9 months.
Experience building software through coursework, internships, research, personal projects, or prior professional experience.
Programming experience in one or more languages such as Python, TypeScript, JavaScript, Go, Java, or C++.
Familiarity with software fundamentals, including object-oriented design, design patterns, data structures, algorithms, API/interface design, and engineering best practices.
Exposure to building applications, services, data pipelines, or user-facing tools in a collaborative environment.
Ability to learn new technologies, reason about technical tradeoffs, and communicate clearly with engineering and cross-functional partners.
Interest in autonomous vehicles, robotics, machine learning, data-centric AI, or developer and ML platform technologies.

Nice To Haves

Graduation between December 2025 and August 2026, with availability to begin employment in 2026.
Experience shipping software or features through internships, research, academic projects, or prior professional work.
Experience with technologies such as Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, WebGL, or similar tools.
Familiarity with scalable software system design, data modeling, API/interface design, observability, CI/CD, or test-driven development.
Experience with computer vision, machine learning, or data-centric AI projects, especially projects involving data annotation, data quality, or autolabeling workflows.
Familiarity with data labeling or annotation platforms, including annotation user interfaces, workflow engines, or quality systems.
Experience with A/B testing, telemetry, or observability systems used to measure product impact and reliability.
Experience developing data-intensive or visualization-heavy applications.
Familiarity with AI-assisted engineering workflows, including agentic development, knowledge acquisition, documentation generation, debugging, or operational triage.
Experience collaborating with customers, product managers, designers, or user experience researchers.
Passion for self-driving and robotics technology and its potential to transform safety, mobility, and the human experience.

Responsibilities

Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality, including efficiency dashboards, automated quality assurance, and autolabel review tools.
Collaborate with ML engineers to design and integrate ML-driven data annotation, including pre-labeling, autolabeling, and active learning loops.
Help evolve labeling workflows from human-only processes toward machine-led labeling at scale.
Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies.
Ship features spanning multiple product surfaces that improve how quickly and accurately teams can label data for new models and cities.
Learn and apply production engineering practices, including code review, automated testing, observability, CI/CD, and incremental delivery.
Use and contribute to modern AI-assisted development workflows, such as code assistants, automated documentation, test generation, and operational triage, while maintaining code and product quality.
Partner with labelers, ML engineers, Product Operations, Product Management, Data Science, and other cross-functional teams to understand user needs and improve the platform.