Machine Learning Engineer Visa Sponsorship Jobs in California
California is the center of machine learning engineering in the United States, with major hiring concentrated in the San Francisco Bay Area, Los Angeles, and San Diego. Companies like Google, Meta, Apple, and a dense ecosystem of AI startups regularly sponsor H-1B visa and O-1 visas for ML engineers with strong research or production experience.
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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.
Machine Learning Engineer Job Roles in California
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Search Machine Learning Engineer Jobs in CaliforniaMachine Learning Engineer Jobs in California: Frequently Asked Questions
Which companies sponsor visas for machine learning engineers in California?
Large technology companies are the most consistent sponsors. Google, Meta, Apple, Amazon Web Services, Microsoft, and Nvidia all have significant California operations and established visa sponsorship programs for machine learning engineers. Beyond big tech, AI-focused companies like Anthropic, OpenAI, and Scale AI, as well as semiconductor and autonomous vehicle firms, regularly file H-1B petitions for ML roles in the state.
Which visa types are most common for machine learning engineer roles in California?
The H-1B is the most common visa category for machine learning engineers in California, given that ML roles typically require at least a bachelor's degree in computer science, mathematics, or a related field. Candidates with exceptional research records, publications, or competitive awards may qualify for the O-1A. Australians can pursue the E-3 visa as a lower-competition alternative for roles that meet specialty occupation requirements.
Which cities in California have the most machine learning engineer sponsorship jobs?
The San Francisco Bay Area, including San Jose, Mountain View, Menlo Park, and San Francisco itself, accounts for the largest share of ML engineering sponsorship roles in California. Los Angeles has a growing AI and entertainment-tech sector with active hiring. San Diego also sees demand from biotech, defense contractors, and research institutions like UC San Diego that feed into commercial ML roles.
How to find machine learning engineer visa sponsorship jobs in California?
Migrate Mate filters job listings specifically by visa sponsorship availability, making it straightforward to search for machine learning engineer roles in California without sorting through positions that do not sponsor. You can filter by location and role type to surface verified sponsoring employers across the Bay Area, Los Angeles, and San Diego. Creating a profile on Migrate Mate also helps match your background to relevant openings.
Are there any state-specific considerations for machine learning engineers seeking sponsorship in California?
California's concentration of H-1B-dependent employers means some companies must meet additional attestation requirements under DOL rules, which can affect hiring timelines. The state's university pipeline, particularly from Stanford, UC Berkeley, Caltech, and UCLA, creates significant competition for sponsored roles. ML engineers with specialized skills in large language models, computer vision, or MLOps tend to have stronger positioning with sponsors in the current California hiring market.
What is the prevailing wage for sponsored machine learning engineer jobs in California?
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.