Jobs / United States / General Motors Company
2027 Summer Intern – Machine Learning Engineer, AV/AI Platform
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 roles3 certified filings for “Senior Machine Learning Engineer” (Software Developers) in WA: $150k–$230k, median $225k. Most were filed at wage level IV (67%) — 4 lottery entries, ≈61% 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.
Or apply yourself on the official page →
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: GM’s AV/AI Platform organization builds the software foundation and engineering tools that power safe, reliable, and scalable autonomous driving and advanced driver-assistance systems. We are building a state-of-the-art AI training platform in close collaboration with model teams to deliver Autonomous car milestones, maximizing model flop utilization and iteration speed, and enhancing inference systems. We are augmenting training, data processing, evaluation and the deployment loop with a data flywheel to validate performance and continuously improve Autonomous driving and Active Safety. W e accelerate compute performance through architectural changes and low-level optimizations, informed by advanced HIL platforms, AI tooling and workflows and instrumentation built in-house. We streamline compiler/build and modelbench performance; enhance data processing, preprocessing, and batch inference; and improve monitoring and availability to increase velocity and quality. About the Role: As an intern, you will help develop and optimize our AI training platform by improving data processing pipelines, accelerating model training and inference workflows, and/ or enhancing testing infrastructure. You will support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed for advancing autonomous driving performance. What You’ll Do: • Develop scalable infrastructure and tools to support model training, model deployment to the edge devices, egression, and rules-based models, operations, and inference. • Suggest, collect and synthesize requirements and work with your managers or tech leads to create feature roadmap • Code deliverables in tandem with the engineering team. • Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU). • Perform specific responsibilities which vary by team. Required Qualifications: • Currently enrolled in a full-time, degree-seeking program and in the process of obtaining a Bachelor's or Master's degree in computer science, artificial intelligence, machine learning, robotics, engineering, or a related STEM field. • Must be graduating between December 2027 and August 2029 and have at least one additional quarter/semester of school remaining following the completion of the internship. • Availability to work full-time, 40 hours per week, during the 12 week summer internship period between May 24th – August 13th or June 14th – September 3rd 2027 • Strong foundation in algorithms and data structures, with proficiency in C++, Python, or another object-oriented programming language. • Demonstrated coursework, project, research, or applied experience in artificial intelligence or machine learning • Experience with algorithms, software development, or systems engineering fundamentals • Strong written and verbal communication skills and the ability to collaborate across teams. Preferred Qualifications • Demonstrated software engineering experience through an internship, work experience, coding competition, student engineering team or competition (e.g., SAE student competitions, EcoCAR, Formula SAE, or Baja SAE), research, coursework, or personal project. Relevant experience may include vehicle systems, autonomy, robotics, embedded software, controls, simulation, sensor data, or ML; exposure to AI-assisted development tools, agentic workflows, or LLM-powered tools is a plus. • Familiarity with AI –assisted engineering tools (e.g., for code generation, model analysis, or experiment planning) to enhance productivity and understanding of ML systems. • Demonstrated creativity and quick problem-solving