Machine Learning Research Jobs in California
Machine Learning Research jobs in California sit at the center of one of the most active AI and technology labor markets in the world, with demand concentrated in academic research, cloud infrastructure, autonomous systems, and biotech from entry-level research engineers through principal scientists. San Francisco, San Jose, and Los Angeles anchor the highest volume of openings, where employers like Google, Meta, and Stanford University maintain large and sustained research operations. The most sought-after specializations are large language model development, computer vision, reinforcement learning, and ML systems optimization. Find a role that fits below and apply directly.
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Showing 5 of 61+ Machine Learning Research jobs
Location
San Francisco
Employment Type
Full time
Location Type
Hybrid
Department
Research & Engineering
About Liquid AI
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
The Opportunity
You'll join the Pre-Training Data team, which decides what Liquid Foundation Models learn from: which data we source, filter, synthesize, and mix, and in what order. Public data sources are plateauing, so model quality increasingly depends on purpose-built datasets and on knowing which data changes actually help.
We treat data as a research problem, not an infrastructure problem. You'll work alongside our pre-training researchers on a small team, run the experiments that settle data questions, and build the pipelines that carry the answers into training runs.
What We're Looking For
We need someone who:
Thinks like a researcher, ships like an engineer: You build the pipelines and you make the model-facing calls about what goes into them. You don't hand off either half.
Leads with a hypothesis: Before changing a dataset, you can say what it should do to the model, what you'll measure, and what could fool you, such as contamination or a noisy benchmark. You hold views on pre-training pitfalls, and you test them.
Owns problems end to end: You take ambiguous work from open question to shipped result with little direction or process, do the unglamorous parts that get it there, and don't care whose job it was.
The Work
Source web-scale and public datasets, and build the pipelines that filter, deduplicate, and decontaminate them
Design data mixtures and curricula for pre-training and midtraining, and decide what goes into each run
Build synthetic data generation systems using LLMs, structured prompting, and distillation to cover what web data can't
Run proxy experiments and ablations to measure what each data change does to model quality, and decide what scales up
Build evaluation sets you can trust, and keep data lineage clear enough that any training run can be traced and reproduced
Experience & Capabilities
What you'll need:
Deep, hands-on pre-training data work you did yourself: web-scale sourcing and curation, synthetic data generation, mixing and curriculum design, deduplication and decontamination. This is where depth matters most.
The ability to reason from a data decision to its effect on training: how data interacts with model size, architecture, and training dynamics, why more data can hurt, and which changes matter as models scale
Experiments you designed to test data changes, such as baselines, scaling ladders, small-scale proxies, and held-out loss, with a clear account of what a change did and why
Production data pipelines you built and owned, not only research prototypes
Solid ML fundamentals: you've trained, evaluated, and iterated on models in Python (PyTorch preferred)
3+ years of relevant experience with an M.S., 1+ year with a Ph.D., or 5+ years with a B.S.
Helpful, not required:
Distillation, long-context, or architecture-specific data strategy
Designing evaluation frameworks and benchmarks for pretrained models
Data work across both pre-training and post-training
Small, efficient models or on-device deployment
Startup experience, or a track record of connecting model work to product needs
What Success Looks Like (Year One)
You own a critical pre-training data pipeline end to end
Data systems you built or improved have measurably moved model performance
You've identified and integrated at least one external dataset that moved the needle
What We Offer
Upstream of every model: Pre-training data shapes every Liquid Foundation Model. On a small team, the mixtures, filters, and synthetic data you build go straight into the models we ship.
Compensation & Equity: Competitive base salary plus equity, so you share in what we build. Final offers reflect skills, experience, and location.
Time Off: Unlimited PTO, plus company-wide Refill Days, long weekends when the whole company rests together.
Health & Wellbeing: Comprehensive health coverage, with details varying by location.
Paid Leave: Paid leave for pregnancy, parenting, medical needs, and caring for family.
Location
San Francisco
Employment Type
Full time
Location Type
Hybrid
Department
Research & Engineering
About Liquid AI
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
The Opportunity
You'll join the Pre-Training Data team, which decides what Liquid Foundation Models learn from: which data we source, filter, synthesize, and mix, and in what order. Public data sources are plateauing, so model quality increasingly depends on purpose-built datasets and on knowing which data changes actually help.
We treat data as a research problem, not an infrastructure problem. You'll work alongside our pre-training researchers on a small team, run the experiments that settle data questions, and build the pipelines that carry the answers into training runs.
What We're Looking For
We need someone who:
Thinks like a researcher, ships like an engineer: You build the pipelines and you make the model-facing calls about what goes into them. You don't hand off either half.
Leads with a hypothesis: Before changing a dataset, you can say what it should do to the model, what you'll measure, and what could fool you, such as contamination or a noisy benchmark. You hold views on pre-training pitfalls, and you test them.
Owns problems end to end: You take ambiguous work from open question to shipped result with little direction or process, do the unglamorous parts that get it there, and don't care whose job it was.
The Work
Source web-scale and public datasets, and build the pipelines that filter, deduplicate, and decontaminate them
Design data mixtures and curricula for pre-training and midtraining, and decide what goes into each run
Build synthetic data generation systems using LLMs, structured prompting, and distillation to cover what web data can't
Run proxy experiments and ablations to measure what each data change does to model quality, and decide what scales up
Build evaluation sets you can trust, and keep data lineage clear enough that any training run can be traced and reproduced
Experience & Capabilities
What you'll need:
Deep, hands-on pre-training data work you did yourself: web-scale sourcing and curation, synthetic data generation, mixing and curriculum design, deduplication and decontamination. This is where depth matters most.
The ability to reason from a data decision to its effect on training: how data interacts with model size, architecture, and training dynamics, why more data can hurt, and which changes matter as models scale
Experiments you designed to test data changes, such as baselines, scaling ladders, small-scale proxies, and held-out loss, with a clear account of what a change did and why
Production data pipelines you built and owned, not only research prototypes
Solid ML fundamentals: you've trained, evaluated, and iterated on models in Python (PyTorch preferred)
3+ years of relevant experience with an M.S., 1+ year with a Ph.D., or 5+ years with a B.S.
Helpful, not required:
Distillation, long-context, or architecture-specific data strategy
Designing evaluation frameworks and benchmarks for pretrained models
Data work across both pre-training and post-training
Small, efficient models or on-device deployment
Startup experience, or a track record of connecting model work to product needs
What Success Looks Like (Year One)
You own a critical pre-training data pipeline end to end
Data systems you built or improved have measurably moved model performance
You've identified and integrated at least one external dataset that moved the needle
What We Offer
Upstream of every model: Pre-training data shapes every Liquid Foundation Model. On a small team, the mixtures, filters, and synthetic data you build go straight into the models we ship.
Compensation & Equity: Competitive base salary plus equity, so you share in what we build. Final offers reflect skills, experience, and location.
Time Off: Unlimited PTO, plus company-wide Refill Days, long weekends when the whole company rests together.
Health & Wellbeing: Comprehensive health coverage, with details varying by location.
Paid Leave: Paid leave for pregnancy, parenting, medical needs, and caring for family.
See All 61 Machine Learning Research Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find JobsMachine Learning Research Jobs by City in California
Where California roles are concentrated, by current openings.
Machine Learning Research Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- Meta11
- Nuro4
- Achira3A
- Rad AI3
- Scale AI3
Top Industries Hiring
- Technology & Software
- Electronics & Hardware
- Artificial Intelligence
- Transportation & Logistics
- Biotechnology & Pharmaceuticals
What California Employers Look For
The qualifications that appear most often in machine learning research jobs across California.
- PhD or master's degree in machine learning, computer science, or a closely related field
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing and training large-scale neural network models on real datasets
- Demonstrated research output such as peer-reviewed publications or conference papers
- Ability to work with distributed computing systems and cloud platforms like GCP or AWS
- Strong mathematical foundation in linear algebra, probability, and optimization theory
Machine Learning Research Jobs in California: Frequently Asked Questions
How do you become a machine learning research in California?
Most machine learning research roles in California require a master's or PhD in computer science, machine learning, or statistics from an accredited university, though some companies hire candidates with a strong bachelor's degree and demonstrated project experience. California does not issue a state license for this role. What hiring managers at California research labs and technology companies consistently look for is a portfolio of original work, whether through graduate research, published papers, or open-source contributions that demonstrate independent problem-solving.
Which companies hire machine learning researchs in California?
Employers hiring machine learning researchs in California right now include Meta, Nuro, and Achira, based on current listings on Migrate Mate as of November 2026. California's dense concentration of AI-focused technology companies, research universities, and well-funded biotech firms means sustained demand across both industry labs and academic institutions.
Which California cities have the most machine learning research jobs?
San Francisco, Menlo Park, and Mountain View have the most machine learning research openings in California. The San Francisco Bay Area dominates because it is home to the headquarters of major AI-first technology companies and university research programs at Stanford and UC Berkeley, while Los Angeles has grown substantially as entertainment technology, autonomous vehicles, and healthcare AI companies have expanded their research teams there.
Are there remote machine learning research jobs in California?
Yes, and more than most fields, since the core work of designing models, running experiments, and writing papers translates well to remote environments. About 48% of machine learning research openings tied to California are remote or hybrid as of November 2026, reflecting how broadly technology employers in the state have adopted flexible arrangements. The most consistently remote portions of the role are individual experiment runs, literature review, and code development, while collaborative whiteboard sessions and hardware-dependent work tend to remain on-site.
How can I get hired as a machine learning research in California with little or no experience?
The most realistic entry path is a strong master's thesis or independent research project that you can discuss concretely in interviews, paired with an application to research engineer or associate researcher programs at California-based companies. Google Brain, Meta AI, and Apple all run structured new-graduate research programs from their California campuses, and UC-affiliated national labs such as Lawrence Berkeley and LLNL offer postdoctoral or student researcher appointments that serve a similar function. Candidates moving from adjacent roles like data scientist or software engineer strengthen their position by building a public project demonstrating original model design, even if it was completed outside a formal research setting.
Where can I find and apply to machine learning research jobs in California?
You can find and apply to machine learning research jobs in California on Migrate Mate, which lists current openings tied to California employers. Find roles that match your background and apply directly to the ones that fit.
See All 61 Machine Learning Research Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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