Jobs / United States / General Motors Company
Staff Cloud and AI Solutions Architect
General Motors Company · 🇺🇸 2 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 roles4 certified filings for “Senior Software Safety Architect” (Software Quality Assurance Analysts and Testers) in CA: $220k–$220k, median $220k. Most were filed at wage level IV (100%) — 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 Posting summary Production Mapping is looking for a Staff Cloud and AI Solutions Engineer to build and scale future-ready mapping foundations that support the expansion of our mapping databases and the next generation of software-defined vehicle experiences. This role will combine deep software and cloud engineering with data-platform architecture and practical AI enablement. You will design scalable foundations for mapping data, create reusable services and workflows, and help transform the team’s existing knowledge, tools, and engineering practices into secure, production-ready Agentic AI solutions. Your work will help mapping teams move faster, improve data quality and traceability, and use cloud-based intelligence in their day-to-day development and operations. The ideal candidate is a hands-on technical leader who can work across data engineering, backend services, cloud infrastructure, distributed systems, developer tooling, and AI-enabled workflows. Experience in automotive, mapping, ADAS, SDV, robotics, or other data-intensive domains is highly desirable. The role As a Staff Cloud and AI Solutions Engineer, you will provide technical leadership for the solutions, services, and engineering patterns that make Production Mapping data more scalable, trusted, discoverable, and useful. You will help define the architecture for a future-ready mapping database ecosystem, including data ingestion, transformation, storage, access, quality, lineage, governance, and delivery to downstream consumers. You will also identify practical opportunities to apply AI and agentic workflows to engineering work, using the knowledge base that already exists across documentation, code, metadata, operational data, and team practices. This is a senior individual-contributor role with broad influence and hands-on delivery responsibility. You will set technical direction, build reference implementations, establish reusable standards, and partner with multiple teams to move solutions from concept to production. Success will come from creating durable capabilities that teams adopt—not from becoming the owner of every mapping system or every AI initiative. What you’ll do • Define and evolve the target architecture for scalable mapping data foundations, including data models, storage patterns, ingestion and transformation pipelines, APIs, data access, metadata, lineage, quality controls, and governance. • Build, productionize, and scale reusable cloud-native solutions and services that support the growth, availability, performance, security, and cost efficiency of mapping databases. • Establish data contracts, validation frameworks, observability, and operational standards that make mapping data trustworthy and easier to consume across engineering teams. • Design and implement cloud-first solutions using infrastructure as code, automated deployment, containerized services, CI/CD, monitoring, and production-readiness practices. • Partner with map creation, map delivery, validation, simulation, embedded software, data science, and platform teams to understand their data needs and deliver integrated solutions. • Identify high-value opportunities to apply AI to everyday engineering workflows, including data discovery, technical search, map-data analysis, validation support, diagnostics, release readiness, incident triage, and engineering productivity. • Design and productionize knowledge-grounded Agentic AI solutions that can use approved documentation, code, metadata, telemetry, and operational knowledge to support multi-step engineering tasks. • Help define the architecture and operating model for smart agents, including retrieval, tool use, orchestration, access controls, evaluation, observability, human oversight, and safe deploymen