AI Product Engineer Jobs in USA with Visa Sponsorship
AI Product Engineers are strong H-1B visa candidates, the role requires a computer science or engineering degree and sits squarely within USCIS specialty occupation criteria. Employers at AI-focused startups and established tech companies regularly sponsor H-1B, O-1, and L-1 visas for this title. For detailed occupation requirements, see the O*NET profile.
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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 Visa Sponsorship as an AI Product Engineer
Target AI-native companies first
Companies building AI products as their core business, not as a side initiative, file more LCAs for this title and have established sponsorship pipelines. Their legal teams already know the H-1B process, which reduces friction and delays for new hires.
Align your degree field to the role
USCIS requires a direct relationship between your degree and the job. Computer science, software engineering, and machine learning degrees map cleanly. A business degree, even with a technical minor, may trigger an RFE for this specialty occupation title.
Document your AI-specific experience before interviews
Prepare a concise summary of your product contributions: models you've shipped, inference infrastructure you've built, and measurable outcomes. Employers assessing sponsorship risk want evidence you can contribute immediately, which shortens internal approval timelines.
O-1A is a realistic alternative for experienced engineers
If you have published research, patents, open-source projects with significant adoption, or a track record of leading AI initiatives, the O-1A visa bypasses the lottery entirely. Immigration counsel can assess your qualifications based on documented achievements.
Clarify sponsorship scope before accepting an offer
Some employers will file an H-1B petition but won't cover premium processing or legal fees. Others offer full sponsorship through green card. Ask specifically what's included so you can evaluate the offer with complete information before signing.
AI Product Engineer jobs are hiring across the US. Find yours.
Find AI Product Engineer JobsFrequently Asked Questions
Does AI Product Engineer qualify as a specialty occupation for H-1B purposes?
Yes. USCIS consistently approves H-1B petitions for AI Product Engineer roles when the position requires a bachelor's degree or higher in computer science, software engineering, or a closely related field. The key is that the job description must demonstrate the degree is a minimum requirement, not a preference. Generic language like 'degree preferred' rather than 'degree required' has triggered RFEs in this category, so the offer letter and LCA documentation matter significantly.
Which visa types do employers commonly use to sponsor AI Product Engineers?
H-1B is the most common path, particularly for candidates already in the U.S. on F-1 OPT or STEM OPT. O-1A is increasingly viable for engineers with notable research publications, patents, or open-source contributions that demonstrate extraordinary ability. L-1B works for engineers transferring from a foreign office of the same company. Candidates from Australia may also qualify for the E-3, which has no lottery and significantly faster processing.
What degree field do I need to get sponsored as an AI Product Engineer?
Computer science, software engineering, electrical engineering, or machine learning are the most defensible degree fields for this title. Data science degrees are generally accepted. A degree in a non-technical field, even with extensive self-taught experience, creates sponsorship risk because USCIS evaluates the formal credential, not practical skill. If your degree field is adjacent rather than direct, a credential evaluation and employment history letter from an expert witness can strengthen the petition.
Are AI Product Engineer roles harder to get sponsored for at startups versus large tech companies?
Large companies have dedicated immigration teams and established H-1B processes, which makes the internal approval faster and the petition more polished. Early-stage startups often sponsor successfully too, but they may lack prior LCA filings, which USCIS treats as a neutral factor, not a negative one. The real risk at startups is financial stability: USCIS scrutinizes whether the employer can pay the prevailing wage for the full petition period. Series A and later-stage startups with clear funding runway typically sponsor without difficulty.
Where can I find AI Product Engineer roles that offer visa sponsorship?
Migrate Mate lists AI Product Engineer positions from employers who have confirmed they sponsor work visas, so you're not filtering through hundreds of roles that don't apply to your situation. The platform is built specifically for international candidates, which means job listings surface sponsorship details that generic job boards omit. Browsing by role title on Migrate Mate shows you which companies are actively hiring and willing to sponsor right now.
What is the prevailing wage requirement for sponsored AI Product Engineer jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.
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