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Scientific Machine Learning Jobs in California (NOW HIRING)

Machina Labs

Machine Learning Engineer

Chatsworth, CA

$160K - $190K/yr

Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field with 6+ years of hands-on experience in machine learning and AI; or a Ph.D. in a relevant ...

Machina Labs

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field with 6+ years of hands-on experience in machine learning and AI; or a Ph.D. in a relevant ...

Apple

Machine Learning Engineer - Health AIML

Cupertino, CA · On-site

$212K - $318K/yr

The Health AI team is at the forefront of machine learning and health science at Apple. We are a close-knit team of highly accomplished, deeply technical research scientists, software engineers, and ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist. * Proficiency across topics in machine learning and statistics.

Pattern AI

Machine Learning Engineer

San Mateo, CA

Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist. * Proficiency across topics in machine learning and statistics.

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in California? For Scientific Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in California look for? The top searched job categories for Scientific Machine Learning jobs in California are:
What cities in California are hiring for Scientific Machine Learning jobs? Cities in California with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in California as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Machine Learning Scientist, Reinforcement Learning

Profluent

Emeryville, CA • On-site, Remote

$200K - $330K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 8 days ago

Job description

Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to design and validate novel, functional proteins to revolutionize biomedicine. Based in Emeryville, CA, we are backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures, and have raised over $150M to date.
We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into reinforcement learning for biomolecular design. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain.
As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists.
Responsibilities
  • Design and develop state-of-the-art online and offline reinforcement learning algorithms for protein design
  • Collaborate across the machine learning and protein design teams to adapt and improve reinforcement learning techniques from other domains to protein design
  • Architect, implement, and optimize core infrastructure to support the post-training of protein language models
  • Curate relevant datasets and design tasks for rigorous evaluation of generative models
  • Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings
  • Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company

Qualifications
  • PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field
  • Experience with conceiving of, implementing, and evaluating novel machine learning and reinforcement learning techniques
  • Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS)
  • Experience with modern deep learning frameworks such as Pytorch or Jax

Preferences (but not required)
  • Familiarity with foundational biology of proteins and nucleic acids
  • Experience developing machine learning models for proteins (language models, structure prediction, design)
  • Experience with cloud compute platforms (GCP, AWS, Azure, OCI)
  • Previous experience in data extraction and curation from bioinformatics data sources
  • Familiarity with wet lab experimental assays and associated limitations

What We Offer
  • High-growth opportunity with meaningful impact on the future of protein design
  • Competitive compensation package with equity participation
  • 401(k) with a strong employer match
  • Comprehensive benefits including health/dental/vision insurance
  • Generous PTO policy and commitment to work-life balance
  • Professional development opportunities in a cutting-edge field at the intersection of AI and biology

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.
Employment Eligibility Verification
Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.
Hiring Salary Range
$200,000-$330,000 USD

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