AI Product Engineer Green Card Jobs
AI Product Engineer roles sit squarely in EB-2 and EB-3 territory: employers file a PERM labor certification with the DOL, then sponsor your I-140 petition for permanent residency. Most AI product roles require a bachelor's or master's in computer science, making green card sponsorship straightforward to document and defend.
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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 Green Card Sponsorship as an AI Product Engineer
Align your credentials to PERM job requirements
Before applying, confirm your degree field maps directly to the posted AI Product Engineer job description. PERM audits scrutinize whether your foreign credential matches the minimum stated requirement, so have a credential evaluation ready.
Target employers with active EB-2 and EB-3 filing history
Search PERM disclosure data through the OFLC Wage Search to identify employers who have recently sponsored AI or product engineering roles. Past filings signal an internal process already exists, which shortens your path to an offer.
Use Migrate Mate to filter for green card sponsoring roles
Search AI Product Engineer positions on Migrate Mate to surface employers with documented PERM sponsorship history. This removes guesswork and lets you focus applications on companies already equipped to handle employment-based green card filings.
Negotiate the PERM filing timeline before signing
Ask during the offer stage when the employer plans to initiate PERM. Some companies wait 12 to 18 months after your start date. Confirming the timeline upfront protects you if your current visa status has a fixed end date.
Document your AI product work for the EB-2 advanced degree track
Gather transcripts, employer verification letters, and performance records now. USCIS requires clear evidence that the role demands an advanced degree in a specific specialty, and AI product engineering increasingly qualifies under that standard.
Understand how EB-3 backlog affects your country of birth
For nationals of India or China, EB-3 priority dates can lag years behind the current filing date. Review the USCIS Visa Bulletin before accepting an offer so you can weigh whether concurrent I-485 filing or consular processing is realistic for your situation.
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Find AI Product Engineer JobsAI Product Engineer Green Card Sponsorship: Frequently Asked Questions
Does an AI Product Engineer role qualify for EB-2 or EB-3 sponsorship?
Most AI Product Engineer positions qualify for EB-2 sponsorship because they typically require a master's degree or a bachelor's degree plus progressive experience in machine learning, software engineering, or a closely related specialty. If the employer's minimum requirement is a bachelor's degree with no advanced degree preference, the role may be filed under EB-3 instead. The classification depends on what the employer lists as the actual job requirement, not what you personally hold.
How is green card sponsorship different from H-1B for this role?
H-1B is a temporary status with a six-year base limit and an annual lottery at the cap-subject level. Green card sponsorship through PERM leads to permanent residency with no renewal cycle and no cap lottery for most applicants. The tradeoff is timeline: PERM labor certification, I-140 approval, and visa availability can take two to five years or more depending on your birth country, while H-1B status can start within months of an approved petition.
What does the PERM process look like for an AI Product Engineer?
Your employer files a labor certification application with the DOL, demonstrating through a recruitment audit that no qualified U.S. worker was available for the role. For AI Product Engineer positions, the job description must specify a defined specialty, typically computer science, artificial intelligence, or software engineering. Once DOL certifies the PERM, your employer files an I-140 immigrant petition with USCIS. You can then adjust status or pursue consular processing once a visa number is available.
How do I find employers willing to sponsor an AI Product Engineer for a green card?
Search Migrate Mate to identify companies with active PERM and employment-based sponsorship history for AI and product engineering roles. You can also cross-reference OFLC disclosure data using the OFLC Wage Search to verify which employers have filed PERM applications in your target job title and location. Prioritizing these employers saves time and reduces the risk of an employer declining to sponsor mid-process.
Can I use O*NET data to strengthen my AI Product Engineer PERM application?
O*NET provides occupational data that employers and immigration attorneys use to define the duties, required knowledge, and degree fields for PERM job descriptions. Referencing O*NET for the Software Developer or Computer and Information Research Scientist occupation codes helps align your employer's job posting with DOL expectations, reducing the risk of an audit or denial based on vague or overly broad job requirements.
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