H-1B Visa AI Product Engineer Jobs
AI Product Engineer roles sit squarely within H-1B specialty occupation requirements, demanding at least a bachelor's degree in computer science, AI, or a related field. Employers file your H-1B petition, pay the DOL-certified prevailing wage, and sponsor your status through each two-year or three-year renewal cycle.
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Get Access To All JobsINTRODUCTION
We're seeking a Lead, AI Engineer to independently design, build, and deploy AI-powered products and workflows that deliver real operational savings and improvements across Rivian's Facilities organization. This is a new kind of role — part product owner, part developer, part designer — built for the era of LLM-augmented work. You won't wait for engineering bandwidth. You'll use AI-native tools like Cursor, Gemini, Claude, and Glean to independently ship working solutions, closing the gap between an organizational problem and a working product. You own your solutions end-to-end: from identifying and scoping a costly manual process, to building the automation that replaces it, to proving the benefits after deployment.
ROLE AND RESPONSIBILITIES
Build & Deploy
- Design, build, and deploy internal applications, agents, and multi-step automations using LLM-assisted development tools (Cursor, Gemini, Claude, Glean, etc.) targeting the highest-cost manual processes across the Facilities org, such as project reporting, cost tracking, change order management, schedule forecasting, document review, cross-functional coordination, and vendor coordination
- Connect Facilities platforms (ACC, Procore, Kahua, FOS, Databricks) via APIs and MCP integrations to create seamless, intelligent workflows that unify siloed data and eliminate duplicate work
- Stand up production-ready enterprise solutions where speed and simplicity are prioritized over engineering complexity
- Own the Full SDLC by applying traditional product development rigor to AI-generated code. You will manage sprint cycles, define technical requirements in Jira, and oversee the end-to-end lifecycle of the tools you build
- Engineering Excellence & Security: Act as the ultimate gatekeeper for quality. You will conduct rigorous code reviews on both human- and AI-written code, ensuring enterprise-grade security, scalability, and clean UI/UX design
- Translate ambiguous operational problems from the Facilities team into well-structured technical architecture, using AI tools not as a crutch, but as an accelerator for rapid prototyping and deployment
- Quantify the value of every major solution: hours saved, cost avoided, errors eliminated. If you can't measure it, rethink the approach
Mentor & Enable
- Coach Facilities team members who are developing their own AI solutions, helping them get over technical and conceptual hurdles
- Contribute to informal workshops, demos, and office hours to grow AI fluency across the Facilities organization
- Create reusable skills, plugins, playbooks, and how-to guides so that good solutions scale beyond a single use case
Partner & Translate
- Partner with cross-functional Facilities stakeholders such as project managers, construction and design leads, real estate, ops, and finance to identify the operational bottlenecks that create the most risk or opportunity for improvement
- Maintain a deep working knowledge of the Facilities tech stack (Autodesk Construction Cloud, Revit, Kahua, and proprietary system) to build solutions that fit how people actually work
- Communicate impact to leadership in business terms — dollars, days, headcount equivalents — not just technical metrics
BASIC QUALIFICATIONS
- 7+ years in a technical role — software development, product ownership, technical program management, or similar
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field; OR equivalent practical, hands-on experience in lieu of a degree
- Demonstrated experience building enterprise apps with AI-assisted coding tools (Cursor, GitHub Copilot, Claude Code, OpenAI Coxed, and equivalent)
- Working knowledge of prompt and skill engineering, AI agent design and orchestration, and LLM application development
- Ability to connect systems via APIs and configure workflow automations end-to-end
- Strong UI/UX instincts — can produce functional, clean interfaces without a design team
- Excellent communication skills; equally comfortable in a whiteboard session with leadership or a working session with ops teams
- Self-directed; thrives in ambiguous environments and can quantify and communicate the business impact of technical work in terms of cost savings, time reduction, and operational efficiency
PREFERRED QUALIFICATIONS
- Familiarity with large-scale construction, real estate, or capital programs
- Experience with enterprise AI tools like Glean or similar knowledge management platforms
- Exposure to MCP (Model Context Protocol) frameworks and multi-agent architectures
- Prior experience shipping internal tools in a non-engineering business unit
- Track record of upskilling peers or running internal training on new technologies
LI-Hybrid
INTRODUCTION
We're seeking a Lead, AI Engineer to independently design, build, and deploy AI-powered products and workflows that deliver real operational savings and improvements across Rivian's Facilities organization. This is a new kind of role — part product owner, part developer, part designer — built for the era of LLM-augmented work. You won't wait for engineering bandwidth. You'll use AI-native tools like Cursor, Gemini, Claude, and Glean to independently ship working solutions, closing the gap between an organizational problem and a working product. You own your solutions end-to-end: from identifying and scoping a costly manual process, to building the automation that replaces it, to proving the benefits after deployment.
ROLE AND RESPONSIBILITIES
Build & Deploy
- Design, build, and deploy internal applications, agents, and multi-step automations using LLM-assisted development tools (Cursor, Gemini, Claude, Glean, etc.) targeting the highest-cost manual processes across the Facilities org, such as project reporting, cost tracking, change order management, schedule forecasting, document review, cross-functional coordination, and vendor coordination
- Connect Facilities platforms (ACC, Procore, Kahua, FOS, Databricks) via APIs and MCP integrations to create seamless, intelligent workflows that unify siloed data and eliminate duplicate work
- Stand up production-ready enterprise solutions where speed and simplicity are prioritized over engineering complexity
- Own the Full SDLC by applying traditional product development rigor to AI-generated code. You will manage sprint cycles, define technical requirements in Jira, and oversee the end-to-end lifecycle of the tools you build
- Engineering Excellence & Security: Act as the ultimate gatekeeper for quality. You will conduct rigorous code reviews on both human- and AI-written code, ensuring enterprise-grade security, scalability, and clean UI/UX design
- Translate ambiguous operational problems from the Facilities team into well-structured technical architecture, using AI tools not as a crutch, but as an accelerator for rapid prototyping and deployment
- Quantify the value of every major solution: hours saved, cost avoided, errors eliminated. If you can't measure it, rethink the approach
Mentor & Enable
- Coach Facilities team members who are developing their own AI solutions, helping them get over technical and conceptual hurdles
- Contribute to informal workshops, demos, and office hours to grow AI fluency across the Facilities organization
- Create reusable skills, plugins, playbooks, and how-to guides so that good solutions scale beyond a single use case
Partner & Translate
- Partner with cross-functional Facilities stakeholders such as project managers, construction and design leads, real estate, ops, and finance to identify the operational bottlenecks that create the most risk or opportunity for improvement
- Maintain a deep working knowledge of the Facilities tech stack (Autodesk Construction Cloud, Revit, Kahua, and proprietary system) to build solutions that fit how people actually work
- Communicate impact to leadership in business terms — dollars, days, headcount equivalents — not just technical metrics
BASIC QUALIFICATIONS
- 7+ years in a technical role — software development, product ownership, technical program management, or similar
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field; OR equivalent practical, hands-on experience in lieu of a degree
- Demonstrated experience building enterprise apps with AI-assisted coding tools (Cursor, GitHub Copilot, Claude Code, OpenAI Coxed, and equivalent)
- Working knowledge of prompt and skill engineering, AI agent design and orchestration, and LLM application development
- Ability to connect systems via APIs and configure workflow automations end-to-end
- Strong UI/UX instincts — can produce functional, clean interfaces without a design team
- Excellent communication skills; equally comfortable in a whiteboard session with leadership or a working session with ops teams
- Self-directed; thrives in ambiguous environments and can quantify and communicate the business impact of technical work in terms of cost savings, time reduction, and operational efficiency
PREFERRED QUALIFICATIONS
- Familiarity with large-scale construction, real estate, or capital programs
- Experience with enterprise AI tools like Glean or similar knowledge management platforms
- Exposure to MCP (Model Context Protocol) frameworks and multi-agent architectures
- Prior experience shipping internal tools in a non-engineering business unit
- Track record of upskilling peers or running internal training on new technologies
LI-Hybrid
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Get Access To All JobsTips for Finding H-1B Visa Sponsorship as an AI Product Engineer
Map your degree to specialty occupation
USCIS evaluates whether your degree field directly relates to AI product engineering work. If your degree is in a tangential field, pull the O*NET profile for this role to document the degree-job alignment before your employer files.
Filter employers by LCA filing history
Use Migrate Mate to filter AI Product Engineer roles by verified DOL Labor Condition Application filings, so you're targeting employers who have already certified prevailing wages for this occupation, not just posted the job title.
Verify prevailing wage before negotiating
Run the OFLC Wage Search using SOC code 15-1252 before you enter salary conversations. Knowing the Level I through Level IV wage bands for your metro area lets you negotiate from a documented baseline, not guesswork.
Ask about cap-exempt status early
Universities, nonprofit research organizations, and government research entities are cap-exempt, meaning they can file your H-1B petition outside the annual 85,000-cap lottery. If you're open to those environments, prioritize them in your search to avoid lottery risk entirely.
Confirm premium processing is on the table
AI product roles often have project start dates tied to product cycles. Ask during the offer stage whether the employer will file with USCIS premium processing, which guarantees a decision within 15 business days and reduces scheduling uncertainty on both sides.
Understand the 60-day grace period before accepting
If you're transferring from another H-1B employer, you have a 60-day grace period after your last day to have a new I-129 petition filed. Accepting an offer with a start date outside that window puts your status at risk, so align timelines before signing.
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Find AI Product Engineer JobsAI Product Engineer H-1B Visa: Frequently Asked Questions
Does an AI Product Engineer role qualify as an H-1B specialty occupation?
Yes. AI Product Engineer positions typically require a bachelor's degree or higher in computer science, artificial intelligence, machine learning, or software engineering, which satisfies USCIS's specialty occupation standard. The role must require that specific degree, not just any bachelor's degree, so your employer's petition should document the direct relationship between the degree field and the job duties.
How do I find employers who actively sponsor H-1B visas for AI Product Engineer roles?
Browse AI Product Engineer listings on Migrate Mate, which surfaces roles from employers with verified DOL Labor Condition Application filing history in this occupation. LCA filings are the strongest indicator that an employer is prepared to sponsor, because each LCA represents a completed DOL wage certification for a specific role at a specific worksite.
What happens to my H-1B if my AI Product Engineer role shifts significantly after I'm sponsored?
A material change in job duties, location, or employer can trigger an amended H-1B petition requirement. If your role evolves from AI product engineering into a substantially different function, your employer must file an amended I-129 with USCIS before the change takes effect. Working under significantly different duties without an amendment creates compliance risk for both you and the employer.
Can I switch employers mid-H-1B if I find a better AI Product Engineer opportunity?
Yes, through H-1B portability. Once your initial petition has been pending or approved for at least 180 days and you've been maintaining valid status, you can start work with a new employer as soon as they file a new I-129 on your behalf, without waiting for approval. You don't need to restart the lottery, but both employers need to be legitimate H-1B sponsors.
Does working on AI models or datasets that are classified or defense-related affect my H-1B sponsorship?
It can. Roles involving export-controlled technology, classified systems, or ITAR-regulated AI work may require your employer to obtain a license or establish a technology control plan before you can access certain materials. Some employers conducting sensitive government AI work are restricted from sponsoring foreign nationals for those specific positions. Ask about access requirements during the offer stage, before the I-129 is filed.
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