Machine Learning Jobs in USA with Visa Sponsorship
Machine learning roles in the US span academic research labs, big tech research divisions, and applied teams at companies across every industry, all of which regularly sponsor international talent. A strong publication record, conference presentations at venues like NeurIPS or ICML, and demonstrated research contributions significantly strengthen both your job applications and your visa petition. The field rewards deep expertise in areas like deep learning, probabilistic modeling, or optimization, making it one of the most accessible paths for researchers seeking US sponsorship. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsAbout Arc Institute
Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco. Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone. Our two Institute Initiatives reflect this model in action:
Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials.
Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes. More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.
About the Position
We are searching for an exceptional scientific leader to establish a new team within Arc Institute’s Computational Technology Center, serving as the Director, Machine Learning for our Alzheimer's Disease Initiative (ADI). This ambitious initiative spans Arc's Technology Centers and Core Investigator Laboratories and focuses on high-throughput interrogation of neurodegeneration and Alzheimer's disease mechanisms using advanced gene editing and functional genomics approaches. As the Machine Learning Research Lead, ADI, you will spearhead development of sophisticated machine learning foundation models to capture cell states and infer gene regulatory networks and causal relationships to predict therapeutic interventions. This position offers the rare opportunity to build and lead a world-class team while making direct contributions to understanding and potentially treating Alzheimer's disease through state-of-the-art computational biology and machine learning approaches.
About You
You are passionate about machine learning and computational biology, with expertise in applying cutting edge ML approaches to biological systems
You excel at developing interpretable machine learning approaches, such as variational inference and causal modeling methods
You are excited about building and leading a technical team while remaining hands-on with foundation model development and implementation.
You thrive in collaborative, multidisciplinary environments and enjoy working with both computational scientists and wet lab biologists
* You are a continuous learner who stays current with the latest developments in both machine learning and neuroscience
In This Position, You Will
Attract, build and lead a team of exceptional machine learning research scientists dedicated to developing foundation models for cellular systems in Alzheimer's disease
Develop and execute on a roadmap of interpretable machine learning approaches to understand disease mechanisms, with emphasis on variational inference, causal modeling, as well as modern transformer- and diffusion-based architectures
Work closely with experimentalists on brain organoid/spheroid cellular models as well as in vivo models, working with scRNA-seq, Perturb-seq and other datasets to unravel causal gene pathways relevant to Alzheimer’s disease
Develop predictive modeling approaches to identify how perturbations can move cell states from high risk Alzheimer’s profiles back to healthy / low risk states
Collaborate closely with experimental biologists to ensure ML models are grounded in disease biology and can feedback into future experimental strategies
Foster collaborations with external partners in the computational biology and neuroscience communities
* Publish high-impact research through preprints, journal publications, open source code, and presentations at leading conferences
Required Qualifications
PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, or related quantitative field
7+ years of relevant experience with a minimum of 3 years of people management experience
Strong research background with experience in academic settings (university, research institute) and/or biotech/pharmaceutical industry with a focus on scientific innovation
Proven expertise in machine learning applications to biological datasets, with specific experience in single-cell profiling data and foundation model development
Deep experience with interpretable machine learning approaches for biological systems (e.g. variational inference methods).
Advanced technical skills in machine learning frameworks, particularly PyTorch, and ideally experience with model training at scale
Publications in top-tier journals in computational biology and machine learning
Excellent communication skills with ability to present complex machine learning concepts to both computational and biological audiences
Proven ability to remain technically hands-on while providing effective team leadership, mentorship, and management
Background in neurodegeneration research including familiarity with Alzheimer's disease datasets, pathways, networks, disease mechanisms, and eQTL analysis is a plus
Compensation
The base salary range for this position is $380,000-$420,000. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.
About Arc Institute
Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco. Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone. Our two Institute Initiatives reflect this model in action:
Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials.
Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes. More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.
About the Position
We are searching for an exceptional scientific leader to establish a new team within Arc Institute’s Computational Technology Center, serving as the Director, Machine Learning for our Alzheimer's Disease Initiative (ADI). This ambitious initiative spans Arc's Technology Centers and Core Investigator Laboratories and focuses on high-throughput interrogation of neurodegeneration and Alzheimer's disease mechanisms using advanced gene editing and functional genomics approaches. As the Machine Learning Research Lead, ADI, you will spearhead development of sophisticated machine learning foundation models to capture cell states and infer gene regulatory networks and causal relationships to predict therapeutic interventions. This position offers the rare opportunity to build and lead a world-class team while making direct contributions to understanding and potentially treating Alzheimer's disease through state-of-the-art computational biology and machine learning approaches.
About You
You are passionate about machine learning and computational biology, with expertise in applying cutting edge ML approaches to biological systems
You excel at developing interpretable machine learning approaches, such as variational inference and causal modeling methods
You are excited about building and leading a technical team while remaining hands-on with foundation model development and implementation.
You thrive in collaborative, multidisciplinary environments and enjoy working with both computational scientists and wet lab biologists
* You are a continuous learner who stays current with the latest developments in both machine learning and neuroscience
In This Position, You Will
Attract, build and lead a team of exceptional machine learning research scientists dedicated to developing foundation models for cellular systems in Alzheimer's disease
Develop and execute on a roadmap of interpretable machine learning approaches to understand disease mechanisms, with emphasis on variational inference, causal modeling, as well as modern transformer- and diffusion-based architectures
Work closely with experimentalists on brain organoid/spheroid cellular models as well as in vivo models, working with scRNA-seq, Perturb-seq and other datasets to unravel causal gene pathways relevant to Alzheimer’s disease
Develop predictive modeling approaches to identify how perturbations can move cell states from high risk Alzheimer’s profiles back to healthy / low risk states
Collaborate closely with experimental biologists to ensure ML models are grounded in disease biology and can feedback into future experimental strategies
Foster collaborations with external partners in the computational biology and neuroscience communities
* Publish high-impact research through preprints, journal publications, open source code, and presentations at leading conferences
Required Qualifications
PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, or related quantitative field
7+ years of relevant experience with a minimum of 3 years of people management experience
Strong research background with experience in academic settings (university, research institute) and/or biotech/pharmaceutical industry with a focus on scientific innovation
Proven expertise in machine learning applications to biological datasets, with specific experience in single-cell profiling data and foundation model development
Deep experience with interpretable machine learning approaches for biological systems (e.g. variational inference methods).
Advanced technical skills in machine learning frameworks, particularly PyTorch, and ideally experience with model training at scale
Publications in top-tier journals in computational biology and machine learning
Excellent communication skills with ability to present complex machine learning concepts to both computational and biological audiences
Proven ability to remain technically hands-on while providing effective team leadership, mentorship, and management
Background in neurodegeneration research including familiarity with Alzheimer's disease datasets, pathways, networks, disease mechanisms, and eQTL analysis is a plus
Compensation
The base salary range for this position is $380,000-$420,000. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.
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Get Access To All JobsTips for Finding Machine Learning Jobs
Leverage top-tier publications for an O-1 visa
Papers accepted at NeurIPS, ICML, ICLR, or CVPR carry significant weight in an O-1 extraordinary ability petition. Combine publications with citation counts, peer review invitations, and conference keynotes to build a compelling case.
Explore EB-2 NIW if you hold a PhD in ML
ML researchers with doctoral degrees can petition for an EB-2 National Interest Waiver without employer sponsorship. If your work has applications in healthcare, climate, defense, or other areas of national importance, NIW may be a viable green card path.
Target industries beyond big tech
ML talent is in demand in quantitative finance (Two Sigma, Citadel, DE Shaw), pharmaceuticals (Pfizer, Genentech), and autonomous vehicles (Waymo). These industries sponsor aggressively and often pay competitively with major tech companies.
Use cap-exempt positions at national labs
Sandia, Los Alamos, and Oak Ridge National Laboratories run active ML research programs with H-1B cap-exempt positions. No lottery required, and you can file any time of year while working on cutting-edge problems.
Build a strong STEM OPT runway
CS, mathematics, and statistics degrees qualify for STEM OPT - up to 3 years of work authorization. Use that time to publish, contribute to production ML systems, and establish the track record that makes your employer invest in long-term sponsorship.
Highlight your engineering skills alongside research
Employers sponsoring ML roles want candidates who can move models from notebooks to production. Proficiency in PyTorch, distributed training, and ML infrastructure makes you more valuable and strengthens the case for a technical specialty occupation.
Frequently Asked Questions
How important is a publication record for getting sponsored in a machine learning role?
Publications are highly important for research-focused ML roles at organizations like Google DeepMind, Meta FAIR, or university labs. For H-1B visa purposes, they demonstrate specialized knowledge at the level expected of a degree holder. For O-1 visa petitions, publications are one of the core criteria for extraordinary ability. Applied ML roles at companies focused on deploying existing techniques may prioritize engineering skills over publications, so the importance depends on whether the role is research or production-oriented.
Can I transition from academia to an industry ML role in the U.S., and will employers sponsor that transition?
Yes. The academic-to-industry transition is one of the most well-established paths in machine learning. Companies like Google, Meta, Microsoft, and Amazon have research scientist roles specifically designed for people with academic backgrounds and routinely sponsor H-1B and O-1 visas for these hires. If you are currently a postdoc or researcher at a U.S. university, you may already have J-1 visa or H-1B status that can be transferred to an industry employer. Connect your academic work to practical applications during interviews to show awareness of production constraints.
How to find Machine Learning jobs with visa sponsorship?
To find Machine Learning jobs with visa sponsorship, use Migrate Mate, which specializes in connecting international talent with sponsoring employers. Focus on tech companies, startups, and research institutions that commonly sponsor H-1B, O-1, or TN visas for ML engineers, data scientists, and AI researchers. These employers actively seek skilled professionals in machine learning, deep learning, and artificial intelligence roles.
Does the O-1 visa work well for ML researchers?
Yes, the O-1A is one of the strongest visa pathways for ML researchers with solid track records. Published papers at top venues (NeurIPS, ICML, ICLR, CVPR), peer review service for journals and conferences, high citation counts, and significant open-source contributions can all serve as evidence of extraordinary ability. The O-1 has no annual cap and no lottery, and it can be processed in 15 business days with premium processing. Many top AI labs actively support O-1 applications for research hires.
Which ML specializations are most in demand for visa sponsorship?
Large language models, reinforcement learning, and computer vision remain the highest-demand specializations. Emerging areas like geometric deep learning, causal ML, and efficient model architectures are particularly valuable for immigration purposes because the talent pool is extremely small. USCIS evaluates whether the role requires someone with your specific expertise, and niche specializations make that argument easier. Applied specializations like recommendation systems, search ranking, and fraud detection are also heavily sponsored.
Do open-source contributions to ML libraries help with visa petitions?
Yes, particularly for O-1 petitions where they can serve as evidence of original contributions of major significance to the field. Contributions to widely used frameworks like PyTorch, TensorFlow, Hugging Face Transformers, or scikit-learn carry the most weight. Document your contributions with metrics: download counts, GitHub stars, citations in papers, and adoption by major companies. For H-1B petitions, open-source work is less directly relevant but helps demonstrate the specialized depth of your expertise.
What is the prevailing wage requirement for sponsored Machine Learning jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a machine learning in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.