Jobs / United States / General Motors Company
ML Systems Engineer, Data Labeling Engineering - Early Career
General Motors Company · 🇺🇸 Sunnyvale, California, United States of America
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What General Motors Company paid sponsored hires in similar roles2 certified filings for “Senior ML Engineer - Embodied AI Offboard Percepti” (Data Scientists) in CA: $230k–$240k, median $235k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — General Motors Company
The US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for General Motors Company →
About the role
Job Description About the Team 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. About the Role 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. What You’ll Do • 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. Basic Qualifications • 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.