Jobs / United States / General Motors Company
Senior AI-Centric Release & Automation Software Engineer
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 roles1 certified filing for “AI/ML Engineer” (Software Developers) in MA: $187k–$187k, median $187k. Most were filed at wage level II (100%) — 2 lottery entries, ≈31% 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 The Role You will be part of a core team that enables safe, reliable, and scalable releases of the Autonomous Vehicle (AV) software stack through intelligent automation, AI-enabled engineering workflows, and data-driven validation. The mission is to accelerate AV software development and release velo ci ty by redu ci ng manual effort, im pro v ing test and release visibility, and applying AI to engineering pro cesses. In this position, you will collaborate closely with Release Engineers, Systems Engineers, DevOps, QA, and AI/ML teams to design and implement automated release validation pipelines, integrate simulation and hardware-in-loop testing, build engineering metrics, and develop AI-enabled solutions for test analysis, failure classification, defect triage, reporting, and workflow orchestration. You will help establish practical standards for evaluating, governing, and scaling automation and AI solutions while im pro v ing release readiness, software quality, and engineering pro ductivity. If you are passionate about applying intelligent automation and systems thinking to accelerate the development of safe, high-quality ML-driven AV software, we want to talk to you. What You’ll Be Doing • Lead the design and implementation of automation across software development, testing, release, and operational workflows. • Identify opportunities to apply AI, machine learning, and LLM-based tools to im pro v e engineering pro ductivity and de ci sion-making. • Build AI-enabled solutions for test analysis, failure classification, defect triage, documentation, reporting, and workflow orchestration. • Develop and maintain scalable CI /CD integrations supporting simulation, hardware-in-loop, regression, and release validation activities. • Build data pipelines that combine engineering, QA, simulation, test, and release information into actionable insights. • Establish practical methods for evaluating the accuracy, usefulness, traceability, and adoption of AI-enabled engineering tools. • Automate repetitive manual pro cesses and measure im pro v e ments in cycle time, test effi ci ency, defect prevention, and engineering throughput. • Im pro v e visibility into test health, regression trends, flaky tests, failure patterns, and release readiness. • Collaborate with engineering, QA, operations, data, and pro gram teams to understand pain points and deliver effective automation solutions. • Integrate tools such as Jira, GitHub, dashboards, observability platforms, and cloud services into unified engineering workflows. • Help define standards and governance for maintainable, secure, observable, and scalable automation and AI solutions. • Communicate technical findings, pro cess im pro v e ments, and measurable business impact to engineering and leadership stakeholders. What You Must Have • Strong pro fi ci ency in Python and SQL . • Pro v e n experience in CI /CD systems (e.g., GitHub Actions, Jenkins, GitLab, or equivalent). • Hands-on experience developing ELT/ETL pipelines and integrating data from engineering, QA, simulation, and operational systems. • Experience applying AI, machine learning, or LLM-based solutions to im pro v e engineering pro ductivity, test analysis, defect triage, documentation, or de ci sion-making. • Ability to evaluate AI-generated outputs for accuracy, consistency, traceability, and usefulness in engineering workflows. • Strong analytical, debugging, and pro blem-solving skills across large-scale software systems. • Experience integrating simulation or hardware-in-loop testing into automated pipelines. • Track record of cross-functional collaboration across e