J-1 Visa Machine Learning Engineer Jobs
Machine Learning Engineer roles in the United States are typically sponsored under the J-1 visa Research Scholar or Trainee program category, depending on your career stage. Securing sponsorship means a U.S. Department of State-designated organization issues your DS-2019, while your host employer provides the technical training environment.
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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.
See all 30+ J-1 Visa Machine Learning Engineer Jobs
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Get Access To All JobsTips for Finding J-1 Visa Sponsorship as a Machine Learning Engineer
Align your credentials to specialty occupation standards
J-1 Training Plans require your degree field to map directly to the ML engineering work you'll perform. Document coursework in statistics, neural networks, or distributed systems before approaching host employers, since designated sponsors vet this match during DS-2019 review.
Distinguish Research Scholar from Trainee eligibility early
If you hold a postgraduate degree and have an established research record, the Research Scholar category fits better than Trainee. Trainee is capped at 18 months total, which limits ML project timelines that typically run longer.
Target host employers with existing J-1 infrastructure
Use Migrate Mate to find U.S. employers already hosting J-1 exchange visitors in engineering roles. These organizations have signed host agreements with designated sponsors and understand the Training Plan documentation you'll need to complete.
Build a project portfolio that maps to O*NET task definitions
Designated sponsors use O*NET occupation profiles to validate that your ML training objectives match recognized U.S. job duties. Structure your portfolio around model development, data pipeline engineering, and evaluation metrics so your Training Plan reflects documented occupational tasks.
Clarify the two-year home residency requirement before accepting an offer
Research Scholar and Specialist J-1 holders from certain countries, or in government-funded fields, face a two-year home-country return requirement before changing status. Confirm your eligibility for a 212(e) waiver with your designated sponsor before signing an employment offer.
Negotiate the Training Plan scope during offer discussions
The J-1 Training Plan is a legal document your host employer and designated sponsor both sign. Before your start date, confirm that the plan specifies ML-specific rotations, tools, and measurable outcomes, since vague objectives delay DS-2019 issuance and can limit program extensions.
Machine Learning Engineer J-1 Visa: Frequently Asked Questions
Which J-1 program category fits a Machine Learning Engineer role?
It depends on your career stage. Current graduate students pursuing ML research typically qualify under the Research Scholar or Short-Term Scholar category. Early-career professionals who have graduated within the past 12 months and want structured industry training use the Trainee category, which runs up to 18 months. Postdoctoral researchers at universities or national labs usually qualify as Research Scholars through a university-affiliated designated sponsor.
Who actually sponsors the J-1 visa for an ML engineering position?
Your visa sponsor is a U.S. Department of State-designated organization, not your employer. Organizations like IIE, Cultural Vistas, or CIEE issue the DS-2019 form and administer program compliance. Your employer is the host organization that provides the technical training environment and signs the Training Plan. Conflating the two is the most common source of confusion when negotiating job offers.
How do I find U.S. employers that are set up to host J-1 engineers?
Many employers have hosted J-1 exchange visitors before but don't advertise it in job listings. Migrate Mate surfaces roles and employers specifically aligned with J-1 sponsorship pathways, so you can focus your applications on organizations that already have host agreements in place rather than pitching unfamiliar employers on the program from scratch.
Does the two-year home residency requirement apply to ML engineers?
It can. The 212(e) two-year home-country return requirement applies to J-1 holders whose participation was funded by their home government or the U.S. government, or who are nationals of a country designated by the State Department as needing their skills. ML engineers on Research Scholar visas funded by U.S. federal research grants are particularly likely to be subject to this requirement. Confirm your status with your designated sponsor before accepting any offer that leads toward an H-1B visa or green card.
What does a J-1 Training Plan need to include for an ML engineering role?
The Training Plan must specify the ML-related tasks you'll perform in the U.S., the tools and technologies involved, measurable performance benchmarks, and the name of your on-site supervisor. Designated sponsors use this document to verify that the program qualifies as genuine training rather than standard employment. Vague plans that list only general software engineering duties are routinely rejected, delaying DS-2019 issuance.