H-1B Visa AI Researcher Jobs
AI Researcher roles qualify as H-1B visa specialty occupations under the computer and mathematical sciences category, typically requiring at least a master's degree or equivalent in machine learning, computer science, or a related field. Most sponsoring employers file in the annual April lottery, so timing your job search to land an offer by February or March is critical.
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Build the computational infrastructure for discovering how cells organize into tissues.
The Biological Information Architecture (BIA) Lab, led by Dr. Felix Zhou at Vanderbilt University, seeks a strong, self-motivated Research Engineer to help build the software, AI workflows, and computational methods that will power a new generation of biological discovery.
This is not a conventional academic programming role. You will have substantial ownership over the lab’s technical direction: architecting software from the ground up, building agentic AI systems for scientific analysis, developing new computational methods, and turning promising ideas into robust tools used by researchers.
We are interested in strong software engineers from industry, computationally exceptional undergraduate or master’s students, and quantitative scientists who want to work on genuinely difficult scientific problems. We care more about technical ability, curiosity, and evidence that you can build things than academic qualifications.
You will also contribute to publications and grants, help establish engineering practice, mentor trainees, and help identify technologies with potential for open-source release, licensing, or commercialization.
This position is a term position. A term position is a staff appointment established for a defined period of time based on the operational needs of the business unit and Vanderbilt University, generally expected to exceed six months but not extend beyond one year. This position's incumbent will be evaluated at the end of a one year assignment for renewal.
About the Work Unit:
The BIA Lab is a new, interdisciplinary computational biology group built around a founding north star mission: “develop the computational tools to decode and ultimately help reprogram the rules governing how individual cells determine tissue-scale behavior”. The lab pursues this objective through atlas-scale multimodal data analysis: combining advanced microscopy, quantitative cell phenomics, spatial tissue profiling and multi-omics data; first-principles open-minded, curiosity-driven scientific inquiry integrating ideas across disciplines without borders, be it theoretical physics, philosophy, economics, biology, computing or mathematics.
We work closely with an expansive network of internal and external collaborators, and committed to open, well-engineered software for the wider bioimage-analysis and computational biology community. The lab is based in Vanderbilt’s Department of Cell and Developmental Biology and affiliated with the Center for Computational Systems Biology (CCSB), a trans-institutional center spanning Vanderbilt Basic Sciences, Vanderbilt University Medical Center, and the College of Connected Computing, placing the lab within a growing community of computational biologists, imaging scientists, and systems-level researchers on campus.
Though newly launched, the lab has a strong methods track record. We have released a suite of open-source tools used by the community, including u-Segment3D (3D cell segmentation); u-Unwrap3D (geometric computing), u-Protrude3D (surface protrusion analysis), SAM-SPOT and MOSES (phenotyping and motion quantification), and v-InfoTrace (information-flow analysis in images). The opportunity now, with real ownership, is to connect and build these cell-level tools into scalable workflows to reverse-engineer the logic circuitry of tissue signaling.
Why Join:
- Direct influence over software architecture, tooling choices, and engineering culture from day one
- Work at the frontier of agentic AI applied to multi-omics and imaging science, with substantial latitude to prototype and ship your own ideas
- Solve real scientific problems involving large, messy, multimodal datasets
- Work closely with the PI on scientific and technical direction in a high-trust environment with substantial autonomy.
- Build tools that enable non-expert researchers to run sophisticated analyses at scale.
- Shape your own growth path toward technical leadership, scientific software, AI systems, computational biology, or technology development.
- Potential opportunities for open-source releases, technology transfer, licensing, or spin-out development as promising technologies mature.
- Join an interdisciplinary environment without borders where ideas from computing, mathematics, physics, biology, arts and philosophy can directly shape the work.
Key Functions and Expected Performance:
Shape the lab’s technical vision and roadmap
- Define software architecture, development standards, and infrastructure choices for the lab
- Advise the PI on build-vs-adopt decisions and long-term technical strategy
Design and development of agentic workflows for multi-omics and imaging data
- Build agentic, LLM-based pipelines that automate ingestion, quality control, processing, and analysis of multi-omics (genomics, transcriptomics, proteomics) and imaging (light-sheet, high-content, spatial) data
- Integrate agentic workflows with existing lab software and HPC/cloud compute infrastructure
Evaluation, integration, and potential commercialization of new methodologies and tools
- Research and assess suitability of emerging methodologies, algorithms, and open-source packages relevant to the lab’s research direction
- Turn promising research ideas into reliable, reusable software.
- Identify lab-developed technologies with translational or commercial potential and support early-stage technology-transfer, licensing, or spin-out discussions.
Teaching, onboarding, and community engagement
- Onboard and train graduate students, postdocs, and rotation students on lab software and computational workflows.
- Promote and communicate the lab’s software through open-source releases, conferences, GitHub, forums, workshops, and other community channels.
- Contribute to publications and grant writing
Experience and Skills:
Necessary
- Strong software development and technical ability through research, open-source work, internships, industry experience, competitions, or substantial independent projects.
- A bachelor’s degree in a relevant scientific or computational field is sufficient; exceptional candidates with unconventional backgrounds are encouraged to apply.
- Strong Python skills and familiarity with scientific libraries such as NumPy, SciPy, pandas, scikit-image, or scikit-learn.
- Experience developing, releasing, and maintaining software projects.
- Experience with, or strong interest in developing, agentic and LLM-based workflows (e.g., Claude Agent SDK, LangChain, or similar frameworks).
- Strong communication skills and the ability to work effectively with technical and non-technical colleagues.
- Self-motivated, enterprising, intellectually curious, and comfortable operating with an incompletely specified roadmap.
Preferred
- Experience with multi-omics data types (genomics, transcriptomics, proteomics) and/or bioimage analysis (light-sheet, high-content, or spatial imaging).
- Knowledge of workflow orchestration and parallel/distributed computing technologies (e.g., Snakemake, Nextflow, NVIDIA CUDA, OpenMPI, or cloud-based HPC).
- Experience with production-scale software engineering, scientific computing, or large-scale data systems.
Education and Certifications:
- Bachelor’s degree in a relevant scientific or computational field with strong computational emphasis.
- A minimum of 1–2 years of postgraduate or industry experience in software engineering, software development, or a related field is required.
- Exceptional undergraduate and master’s candidates are encouraged to apply, particularly those with substantial software-building experience.
- Master’s or PhD preferred but not required.
Position Summary:
Build the computational infrastructure for discovering how cells organize into tissues.
The Biological Information Architecture (BIA) Lab, led by Dr. Felix Zhou at Vanderbilt University, seeks a strong, self-motivated Research Engineer to help build the software, AI workflows, and computational methods that will power a new generation of biological discovery.
This is not a conventional academic programming role. You will have substantial ownership over the lab’s technical direction: architecting software from the ground up, building agentic AI systems for scientific analysis, developing new computational methods, and turning promising ideas into robust tools used by researchers.
We are interested in strong software engineers from industry, computationally exceptional undergraduate or master’s students, and quantitative scientists who want to work on genuinely difficult scientific problems. We care more about technical ability, curiosity, and evidence that you can build things than academic qualifications.
You will also contribute to publications and grants, help establish engineering practice, mentor trainees, and help identify technologies with potential for open-source release, licensing, or commercialization.
This position is a term position. A term position is a staff appointment established for a defined period of time based on the operational needs of the business unit and Vanderbilt University, generally expected to exceed six months but not extend beyond one year. This position's incumbent will be evaluated at the end of a one year assignment for renewal.
About the Work Unit:
The BIA Lab is a new, interdisciplinary computational biology group built around a founding north star mission: “develop the computational tools to decode and ultimately help reprogram the rules governing how individual cells determine tissue-scale behavior”. The lab pursues this objective through atlas-scale multimodal data analysis: combining advanced microscopy, quantitative cell phenomics, spatial tissue profiling and multi-omics data; first-principles open-minded, curiosity-driven scientific inquiry integrating ideas across disciplines without borders, be it theoretical physics, philosophy, economics, biology, computing or mathematics.
We work closely with an expansive network of internal and external collaborators, and committed to open, well-engineered software for the wider bioimage-analysis and computational biology community. The lab is based in Vanderbilt’s Department of Cell and Developmental Biology and affiliated with the Center for Computational Systems Biology (CCSB), a trans-institutional center spanning Vanderbilt Basic Sciences, Vanderbilt University Medical Center, and the College of Connected Computing, placing the lab within a growing community of computational biologists, imaging scientists, and systems-level researchers on campus.
Though newly launched, the lab has a strong methods track record. We have released a suite of open-source tools used by the community, including u-Segment3D (3D cell segmentation); u-Unwrap3D (geometric computing), u-Protrude3D (surface protrusion analysis), SAM-SPOT and MOSES (phenotyping and motion quantification), and v-InfoTrace (information-flow analysis in images). The opportunity now, with real ownership, is to connect and build these cell-level tools into scalable workflows to reverse-engineer the logic circuitry of tissue signaling.
Why Join:
- Direct influence over software architecture, tooling choices, and engineering culture from day one
- Work at the frontier of agentic AI applied to multi-omics and imaging science, with substantial latitude to prototype and ship your own ideas
- Solve real scientific problems involving large, messy, multimodal datasets
- Work closely with the PI on scientific and technical direction in a high-trust environment with substantial autonomy.
- Build tools that enable non-expert researchers to run sophisticated analyses at scale.
- Shape your own growth path toward technical leadership, scientific software, AI systems, computational biology, or technology development.
- Potential opportunities for open-source releases, technology transfer, licensing, or spin-out development as promising technologies mature.
- Join an interdisciplinary environment without borders where ideas from computing, mathematics, physics, biology, arts and philosophy can directly shape the work.
Key Functions and Expected Performance:
Shape the lab’s technical vision and roadmap
- Define software architecture, development standards, and infrastructure choices for the lab
- Advise the PI on build-vs-adopt decisions and long-term technical strategy
Design and development of agentic workflows for multi-omics and imaging data
- Build agentic, LLM-based pipelines that automate ingestion, quality control, processing, and analysis of multi-omics (genomics, transcriptomics, proteomics) and imaging (light-sheet, high-content, spatial) data
- Integrate agentic workflows with existing lab software and HPC/cloud compute infrastructure
Evaluation, integration, and potential commercialization of new methodologies and tools
- Research and assess suitability of emerging methodologies, algorithms, and open-source packages relevant to the lab’s research direction
- Turn promising research ideas into reliable, reusable software.
- Identify lab-developed technologies with translational or commercial potential and support early-stage technology-transfer, licensing, or spin-out discussions.
Teaching, onboarding, and community engagement
- Onboard and train graduate students, postdocs, and rotation students on lab software and computational workflows.
- Promote and communicate the lab’s software through open-source releases, conferences, GitHub, forums, workshops, and other community channels.
- Contribute to publications and grant writing
Experience and Skills:
Necessary
- Strong software development and technical ability through research, open-source work, internships, industry experience, competitions, or substantial independent projects.
- A bachelor’s degree in a relevant scientific or computational field is sufficient; exceptional candidates with unconventional backgrounds are encouraged to apply.
- Strong Python skills and familiarity with scientific libraries such as NumPy, SciPy, pandas, scikit-image, or scikit-learn.
- Experience developing, releasing, and maintaining software projects.
- Experience with, or strong interest in developing, agentic and LLM-based workflows (e.g., Claude Agent SDK, LangChain, or similar frameworks).
- Strong communication skills and the ability to work effectively with technical and non-technical colleagues.
- Self-motivated, enterprising, intellectually curious, and comfortable operating with an incompletely specified roadmap.
Preferred
- Experience with multi-omics data types (genomics, transcriptomics, proteomics) and/or bioimage analysis (light-sheet, high-content, or spatial imaging).
- Knowledge of workflow orchestration and parallel/distributed computing technologies (e.g., Snakemake, Nextflow, NVIDIA CUDA, OpenMPI, or cloud-based HPC).
- Experience with production-scale software engineering, scientific computing, or large-scale data systems.
Education and Certifications:
- Bachelor’s degree in a relevant scientific or computational field with strong computational emphasis.
- A minimum of 1–2 years of postgraduate or industry experience in software engineering, software development, or a related field is required.
- Exceptional undergraduate and master’s candidates are encouraged to apply, particularly those with substantial software-building experience.
- Master’s or PhD preferred but not required.
See all 936+ H-1B Visa AI Researcher Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa AI Researcher Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship in AI Researcher
Align your degree to the SOC code
USCIS evaluates specialty occupation by matching your degree field to the role's Standard Occupational Classification code. For AI Researcher positions, a degree in computer science, statistics, or a directly related field strengthens the petition and reduces RFE risk.
Search LCA filings before applying
Employers must file a Labor Condition Application with DOL before sponsoring you. Use Migrate Mate to filter AI Researcher roles by verified LCA filing history, so you're only targeting companies that have already committed to the sponsorship process.
Verify the prevailing wage tier before negotiating
Your offered salary must meet the DOL prevailing wage for your role and location. Use the OFLC Wage Search to check the Level I through Level IV wage for AI Researcher positions in your target metro before entering any salary discussion.
Confirm the employer files cap-subject petitions
Universities, nonprofit research organizations, and government research entities are cap-exempt, meaning they can sponsor you outside the April lottery. If you're targeting industry labs, confirm the employer files cap-subject H-1B petitions and has done so recently.
Request premium processing if your start date is fixed
USCIS offers premium processing for H-1B petitions, with a decision guaranteed within 15 business days. If your research role has a hard start date tied to a grant cycle or product launch, ask your employer to elect premium processing to avoid a status gap.
Document publications and patents before the petition
USCIS officers increasingly scrutinize AI Researcher petitions by reviewing whether the role requires theoretical and applied research skills. Compile your publications, conference presentations, and any patent filings now. They won't appear in the petition directly but prepare you to respond to an RFE quickly.
H-1B Visa AI Researcher: Frequently Asked Questions
Does an AI Researcher role qualify as an H-1B specialty occupation?
Yes. AI Researcher positions fall under computer and mathematical occupations in the DOL classification system and routinely satisfy the H-1B specialty occupation standard. USCIS requires that the role normally requires at least a bachelor's degree in a specific, directly related field. A degree in computer science, machine learning, statistics, or a closely related discipline typically meets this standard, though USCIS may issue an RFE if the job description is broad enough to accept unrelated degrees.
Which employers sponsor H-1B visas for AI Researchers?
Both large technology companies and university research labs sponsor AI Researcher H-1B petitions regularly. Universities and affiliated nonprofit research institutions are cap-exempt, which means they can file year-round rather than waiting for the April lottery. Industry employers, including cloud platforms and AI-focused product companies, are cap-subject and must enter the lottery. Browse Migrate Mate to see which employers have active LCA filings for AI Researcher and related research roles.
How does the H-1B lottery affect my timeline as an AI Researcher?
Cap-subject H-1B petitions can only be filed in April for an October 1 start date, following a March registration window. If you're not selected, you must wait until the next lottery cycle unless your employer qualifies as cap-exempt. AI Researchers at universities or government-funded research centers can bypass the lottery entirely. Plan your job search so you have a signed offer and completed LCA by late March to be included in that year's filing.
What documentation should I prepare for an AI Researcher H-1B petition?
Your employer's immigration attorney will build the petition, but you'll need to provide educational transcripts and degree certificates, a detailed description of your research responsibilities, and evidence that the role requires a degree in a specific field. If your degree is from outside the U.S., a credential evaluation is required. Publications, patents, or conference proceedings aren't filed with the petition but can be critical supporting evidence if USCIS issues an RFE questioning whether the position is a specialty occupation.
Can I switch employers mid-project on an H-1B as an AI Researcher?
Yes. H-1B portability under AC21 lets you change employers after your petition has been pending for at least 180 days, as long as the new role is in the same or a similar occupational classification. For AI Researchers, moving between research labs or from a research role to an applied research engineering position typically qualifies. Your new employer must file an H-1B transfer petition before your last day with the current employer to maintain continuous work authorization.