Genai Software Engineer Jobs in USA with Visa Sponsorship
GenAI Software Engineer roles are among the most actively sponsored positions in tech right now. Employers filing H-1B visa petitions for this title consistently cite computer science or a related engineering field as the qualifying degree, making it a strong fit for international candidates on OPT or seeking cap-subject sponsorship. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsWho We Are
Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.
About the Team
We empower engineers to build the future of transportation by transforming how software is developed, tested, and operated using Generative AI and agentic systems. Our team builds the AI infrastructure that enables autonomous coding agents, intelligent assistants, and autoresearch systems across the engineering lifecycle — from code generation and CI/CD to debugging, knowledge discovery, and ML model improvement. We sit at the intersection of engineering productivity and ML advancement, building platforms and self-improving systems that accelerate both.
About the Role
As a Software Engineer in GenAI Infrastructure & Agent Systems, you will design and build platforms that significantly improve developer productivity and ML research velocity. This role spans:
- Agent infrastructure (harnesses, sandboxes, plugin frameworks, MCP integrations) enabling reliable, production-grade AI agents
- Autoresearch systems that run experiments, evaluate results, and propose improvements to core ML models
You’ll work across the stack — from cloud infrastructure and isolation to agent memory, evaluation frameworks, and developer-facing tools.
About the Work
Build AI agent infrastructure
- Design agent orchestration, sandboxing, and isolation systems
- Develop skill/plugin frameworks and integrate internal tools (e.g., CI/CD, code, observability systems)
- Scale compute infrastructure for agent workloads (GCP, Kubernetes)
Develop autoresearch systems
- Build self-improving agents that run experiments, analyze results, and iterate
- Implement memory, feedback loops, and long-horizon reasoning
- Integrate with ML training, evaluation, and data pipelines
Create AI-powered engineering tools
- Build assistants for support, triage, and workflow automation
- Develop tools for code generation, debugging, testing, and PR review
- Detect and resolve performance and reliability issues automatically
Improve quality and developer workflows
- Design evaluation and observability systems for agent performance
- Integrate agents into CI/CD for testing, failure attribution, and remediation
- Build knowledge systems for search, retrieval, and reuse
About You
- 4+ years of industry experience; BS/MS in Computer Science or equivalent practical experience
- Strong programming skills in Python, C++, or Go, with experience building scalable, reliable systems
- Solid background in cloud infrastructure (GCP/AWS), Kubernetes, CI/CD, and developer tooling
- Passion for building production-grade AI agent systems and improving engineering efficiency through automation
- Interest in autonomous, self-improving systems and ML-driven workflows
- Strong systems thinking across infrastructure, memory, and tooling, with ability to work end-to-end
Preferred:
- Experience with LLM/agent systems in production (tool use, orchestration, sandboxing)
- Familiarity with autonomous agent patterns (memory, reflection, planning)
- Experience with MCP, plugin/skill frameworks, or similar architectures
- Background in ML infrastructure (training pipelines, evaluation, experimentation)
- Experience in DevEx/platform engineering, observability, or security isolation patterns
At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.
At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
Who We Are
Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future.
About the Team
We empower engineers to build the future of transportation by transforming how software is developed, tested, and operated using Generative AI and agentic systems. Our team builds the AI infrastructure that enables autonomous coding agents, intelligent assistants, and autoresearch systems across the engineering lifecycle — from code generation and CI/CD to debugging, knowledge discovery, and ML model improvement. We sit at the intersection of engineering productivity and ML advancement, building platforms and self-improving systems that accelerate both.
About the Role
As a Software Engineer in GenAI Infrastructure & Agent Systems, you will design and build platforms that significantly improve developer productivity and ML research velocity. This role spans:
- Agent infrastructure (harnesses, sandboxes, plugin frameworks, MCP integrations) enabling reliable, production-grade AI agents
- Autoresearch systems that run experiments, evaluate results, and propose improvements to core ML models
You’ll work across the stack — from cloud infrastructure and isolation to agent memory, evaluation frameworks, and developer-facing tools.
About the Work
Build AI agent infrastructure
- Design agent orchestration, sandboxing, and isolation systems
- Develop skill/plugin frameworks and integrate internal tools (e.g., CI/CD, code, observability systems)
- Scale compute infrastructure for agent workloads (GCP, Kubernetes)
Develop autoresearch systems
- Build self-improving agents that run experiments, analyze results, and iterate
- Implement memory, feedback loops, and long-horizon reasoning
- Integrate with ML training, evaluation, and data pipelines
Create AI-powered engineering tools
- Build assistants for support, triage, and workflow automation
- Develop tools for code generation, debugging, testing, and PR review
- Detect and resolve performance and reliability issues automatically
Improve quality and developer workflows
- Design evaluation and observability systems for agent performance
- Integrate agents into CI/CD for testing, failure attribution, and remediation
- Build knowledge systems for search, retrieval, and reuse
About You
- 4+ years of industry experience; BS/MS in Computer Science or equivalent practical experience
- Strong programming skills in Python, C++, or Go, with experience building scalable, reliable systems
- Solid background in cloud infrastructure (GCP/AWS), Kubernetes, CI/CD, and developer tooling
- Passion for building production-grade AI agent systems and improving engineering efficiency through automation
- Interest in autonomous, self-improving systems and ML-driven workflows
- Strong systems thinking across infrastructure, memory, and tooling, with ability to work end-to-end
Preferred:
- Experience with LLM/agent systems in production (tool use, orchestration, sandboxing)
- Familiarity with autonomous agent patterns (memory, reflection, planning)
- Experience with MCP, plugin/skill frameworks, or similar architectures
- Background in ML infrastructure (training pipelines, evaluation, experimentation)
- Experience in DevEx/platform engineering, observability, or security isolation patterns
At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.
At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
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Get Access To All JobsTips for Finding Visa Sponsorship as a Genai Software Engineer
Lead with your GenAI stack, not just 'ML experience'
Employers sponsoring GenAI roles want specificity. Name the frameworks you've worked with, such as LangChain, LlamaIndex, or the OpenAI API. Vague references to machine learning experience won't differentiate you in a competitive sponsorship pipeline.
Target companies with active H-1B filing histories
Not every employer that posts a GenAI role will sponsor visas. Focus on companies with documented H-1B filings in this job category. Migrate Mate filters sponsoring employers directly, saving you from applying to roles that will never move forward.
Clarify your OPT or STEM OPT timeline upfront
If you're on F-1 OPT, tell recruiters your authorization end date and whether you qualify for the 24-month STEM extension. GenAI hiring cycles can run three to five months, so timeline transparency prevents late-stage offers falling through.
Align your degree field to the specialty occupation standard
H-1B approval for GenAI engineers requires your degree to relate directly to the role. Computer science, electrical engineering, and applied mathematics are the most defensible fields. Unrelated degrees need strong supplementary evidence to satisfy USCIS specialty occupation criteria.
Negotiate cap-exempt or cap-exempt affiliated employers
Universities, nonprofit research institutes, and their affiliates are exempt from the H-1B lottery. GenAI research roles at these institutions can be filed any time of year, giving you sponsorship access completely outside the annual April lottery cycle.
Prepare a portfolio of production-grade GenAI work
Visa officers and hiring managers both scrutinize whether the role genuinely requires a specialist. Documented work, such as deployed RAG pipelines, fine-tuned models, or agentic systems in production, substantiates the specialty occupation claim and strengthens your petition.
Frequently Asked Questions
Do companies actually sponsor H-1B visas for GenAI Software Engineer roles?
Yes, and at a high rate relative to most engineering titles. GenAI engineering sits squarely within the specialty occupation definition because the role requires a specific theoretical and practical background in machine learning, large language models, or AI systems. Companies ranging from early-stage startups to large technology employers have filed H-1B visa petitions under this and closely related job titles. Migrate Mate lists only employers who are actively willing to sponsor, so you can browse verified opportunities without filtering manually.
What degree do I need for H-1B sponsorship as a GenAI Software Engineer?
A bachelor's degree or higher in computer science, software engineering, mathematics, or a closely related quantitative field satisfies the H-1B specialty occupation requirement for most GenAI roles. Degrees in physics or statistics are generally accepted when paired with demonstrated AI work. If your degree is in an unrelated field, you may still qualify by combining a two-year degree with six years of progressive, specialized GenAI experience under the three-for-one equivalency rule, though this path requires stronger documentation.
How does the H-1B lottery affect my chances of sponsorship in this field?
The H-1B lottery applies to cap-subject petitions regardless of job title. In recent fiscal years, the overall selection rate for the regular cap has been roughly 25 to 30 percent, meaning most registrations go unselected in any given year. For GenAI engineers, two lottery-exempt paths are worth pursuing in parallel: roles at universities or nonprofit research institutions, and L-1 visa transfers if you're already employed abroad by a company with a U.S. entity.
Can I get sponsored on OPT before the H-1B lottery as a GenAI engineer?
Yes. If you graduated from a U.S. university in a STEM field, your initial 12-month OPT period plus the 24-month STEM extension gives you up to three years of work authorization. That window typically covers at least two H-1B lottery cycles, meaning you have more than one opportunity to be selected. Some GenAI employers will also file an O-1A petition if your research contributions, publications, or deployed systems meet the extraordinary ability threshold, bypassing the lottery entirely.
Is the O-1A visa a realistic alternative for GenAI Software Engineers?
For candidates with a strong public profile in AI, it can be. USCIS evaluates O-1A petitions against criteria including published research, speaking at recognized conferences, contributions to open-source projects with significant adoption, or compensation in the top tier for the field. GenAI engineers who have co-authored papers, contributed to widely used frameworks, or built systems cited in industry literature are reasonable candidates. It is not a fit for early-career engineers without documented external recognition.
What is the prevailing wage requirement for sponsored Genai Software 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.