J-1 Visa ML Engineer Jobs
ML Engineer roles in the United States are accessible to exchange visitors through the J-1 visa, typically under the Research Scholar, Trainee, or Intern program categories depending on your career stage. Finding a host employer willing to work with a designated sponsor organization for DS-2019 sponsorship is the critical first step.
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The Chu Lab — Department of Genome Sciences, University of Virginia School of Medicine
The Chu Lab in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence.
ROLE AND RESPONSIBILITIES
Successful candidates will lead one or more of the following ongoing projects:
- Developing neural differential equation and continuous-time dynamical models for spatial and single-cell transcriptomics to dissect cell–cell interactions and perturbation responses in complex tissue microenvironments.
- Building generative models of single-cell and spatial data to characterize cellular and tissue heterogeneity in cancer, inflammation, and tissue senescence.
- Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial-omics data.
Candidates are also encouraged to develop independent research directions aligned with the lab's interests.
About the PI
The lab is led by Dr. Tinyi Chu, who joined UVA as Assistant Professor in 2026. Dr. Chu received his Ph.D. in Computational Biology from Cornell University and subsequently completed postdoctoral training at Memorial Sloan Kettering Cancer Center and Yale University. His work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell, spanning statistical method development, cancer transcriptional regulation, and spatial transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been recognized by a Damon Runyon Quantitative Biology Fellowship and is currently supported by an NIH K99/R00 Pathway to Independence Award (NHGRI) and substantial UVA institutional startup funding — providing a strongly resourced environment for ambitious, long-horizon methodological research.
MENTORSHIP AND CAREER DEVELOPMENT
The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where trainees are valued as scientific collaborators rather than assistants. As a postdoctoral scientist in a newly established lab, you will receive individualized mentorship tailored to your career goals, defined by genuine intellectual exchange, direct technical engagement in algorithm and model development, and shared co-ownership of the science.
- Active Collaboration. The PI maintains an open-door policy, meets regularly with trainees, and is deeply involved to support their algorithm and model development.
- Scientific Independence. You will be supported to develop and lead your own research ideas with the freedom and computational resources required to pursue them.
- Grant Writing and Career Transition. Leveraging the PI's recent successful K99/R00 transition, you will receive step-by-step training in scientific writing, proposal preparation, and fellowship applications. Postdocs are supported and encouraged to apply for independent fellowships.
- Visibility. Full support for presenting at top-tier venues spanning machine learning and computational biology, and active assistance in building your professional network across academia and industry.
ENVIRONMENT
The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine. The lab has full access to UVA's high-performance computing resources and core facilities supporting genomics and imaging.
Charlottesville, Virginia is a highly livable university town nestled at the foothills of the Blue Ridge Mountains, known for its excellent quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.
MINIMUM QUALIFICATIONS
Ph.D. (or equivalent) in Computer Science, Applied Mathematics, Statistics, Computational Biology, Biophysics, Engineering, or a related quantitative discipline, in hand by the appointment start date.
PREFERRED QUALIFICATIONS
- Strong foundational knowledge in mathematics and statistics
- Proficiency in PyTorch (or equivalent deep-learning frameworks)
- At least one peer-reviewed publication in the previous area of research (not necessarily biology-related)
- Genuine intellectual curiosity for solving biological problems through quantitative approaches
- Prior experience with spatial transcriptomics, single-cell omics, or related biological datasets is a plus but not required — candidates from purely computational backgrounds are strongly encouraged to apply; domain-specific biological knowledge can be acquired on the job
This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience.
Postdoctoral employment is temporary and is normally limited to an individual who has been awarded a Ph.D. or equivalent doctorate within the previous five years and who will be involved in full-time research or scholarship at the University. Employment as a Postdoctoral Research Associate is viewed as training and is preparatory for a full-time academic or research career, is supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship during the training period.
This position will sponsor applicants for work visas who meet the qualifications.
Start date is available immediately; the start date is flexible.
This position will remain open until filled. The University will perform background checks on all new hires prior to employment.
TO APPLY:
Please apply through Careers at UVA, and search for R0083959.
Complete an application online with the following documents:
- CV
- Cover letter
- Contact information for 3 references.
Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
For questions about the application process, please contact Bill Crane, Academic Recruiter at Xer5ff@virginia.edu
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA’s commitment to non-discrimination and equal opportunity employment.
INTRODUCTION
The Chu Lab — Department of Genome Sciences, University of Virginia School of Medicine
The Chu Lab in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence.
ROLE AND RESPONSIBILITIES
Successful candidates will lead one or more of the following ongoing projects:
- Developing neural differential equation and continuous-time dynamical models for spatial and single-cell transcriptomics to dissect cell–cell interactions and perturbation responses in complex tissue microenvironments.
- Building generative models of single-cell and spatial data to characterize cellular and tissue heterogeneity in cancer, inflammation, and tissue senescence.
- Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial-omics data.
Candidates are also encouraged to develop independent research directions aligned with the lab's interests.
About the PI
The lab is led by Dr. Tinyi Chu, who joined UVA as Assistant Professor in 2026. Dr. Chu received his Ph.D. in Computational Biology from Cornell University and subsequently completed postdoctoral training at Memorial Sloan Kettering Cancer Center and Yale University. His work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell, spanning statistical method development, cancer transcriptional regulation, and spatial transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been recognized by a Damon Runyon Quantitative Biology Fellowship and is currently supported by an NIH K99/R00 Pathway to Independence Award (NHGRI) and substantial UVA institutional startup funding — providing a strongly resourced environment for ambitious, long-horizon methodological research.
MENTORSHIP AND CAREER DEVELOPMENT
The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where trainees are valued as scientific collaborators rather than assistants. As a postdoctoral scientist in a newly established lab, you will receive individualized mentorship tailored to your career goals, defined by genuine intellectual exchange, direct technical engagement in algorithm and model development, and shared co-ownership of the science.
- Active Collaboration. The PI maintains an open-door policy, meets regularly with trainees, and is deeply involved to support their algorithm and model development.
- Scientific Independence. You will be supported to develop and lead your own research ideas with the freedom and computational resources required to pursue them.
- Grant Writing and Career Transition. Leveraging the PI's recent successful K99/R00 transition, you will receive step-by-step training in scientific writing, proposal preparation, and fellowship applications. Postdocs are supported and encouraged to apply for independent fellowships.
- Visibility. Full support for presenting at top-tier venues spanning machine learning and computational biology, and active assistance in building your professional network across academia and industry.
ENVIRONMENT
The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine. The lab has full access to UVA's high-performance computing resources and core facilities supporting genomics and imaging.
Charlottesville, Virginia is a highly livable university town nestled at the foothills of the Blue Ridge Mountains, known for its excellent quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.
MINIMUM QUALIFICATIONS
Ph.D. (or equivalent) in Computer Science, Applied Mathematics, Statistics, Computational Biology, Biophysics, Engineering, or a related quantitative discipline, in hand by the appointment start date.
PREFERRED QUALIFICATIONS
- Strong foundational knowledge in mathematics and statistics
- Proficiency in PyTorch (or equivalent deep-learning frameworks)
- At least one peer-reviewed publication in the previous area of research (not necessarily biology-related)
- Genuine intellectual curiosity for solving biological problems through quantitative approaches
- Prior experience with spatial transcriptomics, single-cell omics, or related biological datasets is a plus but not required — candidates from purely computational backgrounds are strongly encouraged to apply; domain-specific biological knowledge can be acquired on the job
This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience.
Postdoctoral employment is temporary and is normally limited to an individual who has been awarded a Ph.D. or equivalent doctorate within the previous five years and who will be involved in full-time research or scholarship at the University. Employment as a Postdoctoral Research Associate is viewed as training and is preparatory for a full-time academic or research career, is supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship during the training period.
This position will sponsor applicants for work visas who meet the qualifications.
Start date is available immediately; the start date is flexible.
This position will remain open until filled. The University will perform background checks on all new hires prior to employment.
TO APPLY:
Please apply through Careers at UVA, and search for R0083959.
Complete an application online with the following documents:
- CV
- Cover letter
- Contact information for 3 references.
Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
For questions about the application process, please contact Bill Crane, Academic Recruiter at Xer5ff@virginia.edu
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA’s commitment to non-discrimination and equal opportunity employment.
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Get Access To All JobsTips for Finding J-1 Visa Sponsorship as a ML Engineer
Align your ML specialization with J-1 categories
Research Scholar suits PhD-level ML researchers at universities or labs. Trainee fits early-career professionals with a degree and some work experience. Intern applies to current graduate students. Matching your profile to the right category before applying prevents DS-2019 delays.
Document your ML coursework and project outputs
Designated sponsor organizations require a detailed training plan tied to your field of study. Compile course transcripts, GitHub repositories, published papers, or capstone projects that demonstrate your ML focus before your host employer submits training plan documentation.
Target host employers with existing DS-2019 experience
Search for ML Engineer roles at universities, national labs, and research-driven tech firms that already host J-1 visa exchange visitors. Use Migrate Mate to filter for U.S. employers and roles aligned with J-1 visa sponsorship before reaching out.
Clarify the two-year home residency requirement early
Many ML Engineer J-1 participants, particularly Research Scholars funded by government sources, are subject to the two-year home residency requirement under INA Section 212(e). Confirm whether your program triggers this rule before accepting an offer, as it affects future H-1B visa or green card eligibility.
Verify your host employer can support a training plan
Your host organization must sign a formal training plan with your designated sponsor and demonstrate capacity to supervise your ML work. Confirm this before an offer is extended, since employers unfamiliar with J-1 obligations often withdraw after learning the administrative requirements.
Check DOL wage compliance for your ML role
Although J-1 is not employer-sponsored like H-1B, host employers must pay exchange visitors wages commensurate with similarly situated U.S. workers. Use the OFLC Wage Search and O*NET to benchmark ML Engineer compensation in your target location before negotiating your offer.
ML Engineer J-1 Visa: Frequently Asked Questions
Which J-1 program category fits an ML Engineer role?
It depends on your career stage. Current graduate students pursuing ML internships fall under the Intern category. Early-career professionals with a relevant degree and at least one year of work experience use the Trainee category. PhD-level researchers joining a university lab or national research institute typically qualify under Research Scholar, which is the most common path for senior ML roles.
Who actually sponsors a J-1 visa for an ML Engineer?
The visa sponsor is a U.S. Department of State-designated organization, such as IIE, CIEE, Cultural Vistas, or AIPT, that issues your DS-2019 and monitors program compliance. Your hiring organization is the host employer, not the sponsor. The host must agree to work with a designated sponsor and sign a training plan, but they do not file any petition directly with USCIS.
How do I find U.S. employers open to hosting a J-1 ML Engineer?
Start with Migrate Mate to identify U.S. employers and ML Engineer roles that align with J-1 sponsorship. Prioritize universities, government-affiliated research institutes, and established tech companies with prior J-1 hosting experience. Smaller startups often lack the internal HR infrastructure to navigate the training plan and designated sponsor relationship.
Does the two-year home residency requirement affect ML Engineers on J-1?
It can. The two-year home residency requirement under INA Section 212(e) applies when your J-1 program is government-funded, your home country lists your field on the exchange visitor skills list, or you enter as a government-sponsored visitor. If it applies, you must return to your home country for two years before obtaining an H-1B or immigrant visa unless you receive a waiver.
Can a J-1 ML Engineer work on independent projects or side contracts during their program?
No. J-1 exchange visitors are authorized to work only for the host employer named on their DS-2019 and only in the capacity described in the training plan. Freelance ML contracts, consulting work, or side projects for other organizations are not permitted during your exchange program, regardless of whether the work is remote or unpaid.