Jobs / United States / General Motors Company
Senior Machine Learning Engineer - Mapping
General Motors Company · 🇺🇸 4 Locations
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.
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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 Our Mapping organization is building national-scale, next-generation mapping systems that move beyond static HD maps toward automated, algorithmic and ML-assisted map reconstruction pipelines powered by onboard sensor data. These systems form a critical foundation for localization, perception, simulation, and autonomy at scale. The Role We are looking for a Senior Software Engineer to design and build the mapping algorithms and geospatial data systems behind automated map reconstruction and maintenance within our Mapping Engineering team. In this role, you will develop the algorithms and data models that reconstruct, conflate, validate, and maintain map primitives — lanes, lane connectivity, road network graphs, boundaries, traffic controls, and signs — from large-scale multi-modal sensor data. You will own the geometric and topological correctness of the map itself: how features are represented, how disparate sources are merged, and how errors are detected before they reach the vehicle. This is a hands-on individual contributor role with strong ownership. You will drive technical problems end to end, partner closely with Perception, Localization, and Simulation, and apply machine learning where it measurably improves accuracy, coverage, or automation rate. What You'll Do (Responsibilities) • Design and implement mapping algorithms for map reconstruction and maintenance — lane and boundary extraction, road network graph construction, map conflation and matching, geometry simplification, and topology validation. • Build and evolve geospatial data models for lane-level connectivity at intersections, road network graphs, and associated map attributes and restrictions. • Develop large-scale distributed geospatial pipelines that process sensor-derived and third-party road data into production map releases on a recurring cadence. • Apply machine learning and computer vision models — detection, segmentation, 3D reconstruction, BEV representations — to automate feature extraction and map change detection, and integrate them into production pipelines. • Build automated quality, validation, and regression systems that catch geometric, topological, and semantic map defects before release, with clear accuracy metrics. • Collaborate cross-functionally with Perception, Localization, Simulation, and Platform teams on interfaces, data contracts, and integration points. • Diagnose and resolve system-level issues spanning geospatial data pipelines, algorithms, models, and production workflows. • Contribute to design reviews, engineering best practices, and mentorship of engineers on the team. Minimum Qualifications (Must-Have) • 3+ years of software engineering experience building production systems, with a substantial portion focused on mapping, geospatial, or geometric algorithms. • Strong applied foundation in geospatial and computational geometry concepts — coordinate systems and projections, spatial indexing, geometry operations, map matching, and graph algorithms on road networks. • Demonstrated experience designing geospatial data models and working with road network or map data structures (lanes, segments, intersections, topology). • Hands-on experience with machine learning or computer vision workflow in production — dataset curation, model training or fine-tuning, evaluation, and deployment. • Experience building large-scale distributed data pipelines for geospatial or sensor data. • Proficiency in Python and C++. • BS or MS in Computer Science, GIS, Electrical Engineering, Robotics, or a related technical field, or equivalent industry experience. • Ability to own ambiguous, well-scoped technical problems end to end and drive them to production. Preferred Qualifications (nice to have) •