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Scientific Machine Learning Jobs in California (NOW HIRING)

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

About the role As a Machine Learning Scientist , you will develop cutting-edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. You'll play a ...

Apple

Machine Learning FEA Engineer

San Francisco, CA · On-site

S. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline along with 3+ years of relevant experience Preferred Qualifications Strong expertise in GNNs, CNNs, and ...

Rule14

Be Seen First

Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on predictive modeling, natural language processing, computer vision, recommender systems, and OCR ...

Socket.dev

Machine Learning Engineer

Los Angeles, CA · On-site

$180 - $250/hr

Role Description Founding Data Scientist / Machine Learning Engineer We're looking for a highly ambitious Data Scientist to help build the predictive intelligence layer behind nowfluence. This is not ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$142K - $263K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in California

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 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 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 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 August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Heavy-Ion Physics and Scientific Machine Learning - Postdoctoral Researcher

LLNL

Livermore, CA • On-site

$123K/yr

Full-time

Retirement

Posted 4 days ago

Job description

Company Description
Join us and make YOUR mark on the World!
Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.
Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.
Job Description
We have an opening for a Postdoctoral Researcher to contribute to experimental heavy-ion physics, detector development, and scientific machine learning within Lawrence Livermore National Laboratory's Particle Physics Group. You will play a leading role in the analysis of data from the sPHENIX experiment at Brookhaven National Laboratory and the ATLAS experiment at CERN, while contributing to DOE Office of Science Nuclear Physics (DOE-NP) and Genesis Mission-funded research milestones focused on advanced AI methods for nuclear physics. Research activities span precision measurements of jet quenching, ultra-peripheral collisions, detector performance studies, and the development of next-generation machine learning techniques for particle reconstruction and multimodal data analysis. This position is in the Particle Physics Group within the Nuclear and Chemical Sciences Division.
Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
You will
  • Perform physics analyses using data from the ATLAS and sPHENIX experiments to study the quark-gluon plasma, jet quenching, and heavy-ion collisions.
  • Participate in detector operations, commissioning, calibration, performance studies, and data quality monitoring within the ATLAS and sPHENIX collaborations.
  • Develop reconstruction, simulation, and analysis software using modern C++, Python, ROOT, and HPC/Grid computing resources for processing multi-petabyte experimental datasets.
  • Develop and apply state-of-the-art artificial intelligence and scientific machine learning techniques for particle identification, jet reconstruction, and event interpretation.
  • Contribute to DOE-NP and Genesis-funded project milestones focused on multimodal learning, detector performance improvements, and scalable AI methods for experimental nuclear physics.
  • Present research at collaboration meetings, DOE reviews, workshops, and international conferences.
  • Publish results in leading peer-reviewed journals.
  • Collaborate with physicists, computer scientists, and applied mathematicians across LLNL and external institutions.
  • Perform other duties as assigned.

Qualifications
  • Ph.D. in Physics, Nuclear Physics, High Energy Physics or a closely related discipline.
  • Demonstrated research experience in experimental nuclear or particle physics.
  • Experience analyzing large scientific datasets using ROOT, Python, C++, or similar scientific software frameworks.
  • Experience developing scientific software in Linux environments using modern programming practices.
  • Experience with statistical analysis and uncertainty quantification.
  • Excellent written and verbal communication skills, including publications and scientific presentations.
  • Ability to work effectively in large international collaborations.

Qualifications We Desire
  • Experience with the ATLAS, sPHENIX, RHIC, LHC, CMS, ALICE, or STAR collider experiments.
  • Experience with jet physics, heavy-ion collisions, and detector performance studies.
  • Experience in scientific machine learning, deep learning, foundation models, or multimodal AI.
  • Experience with GPU programming, high-performance computing, distributed computing, or large-scale workflow management.
  • Experience developing reconstruction, simulation, or detector calibration software.
  • Familiarity with modern machine learning frameworks such as PyTorch and TensorFlow.

Pay Range
$123,048 Annually
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.
Additional Information
#LI-Hybrid
Position Information
This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.
Why Lawrence Livermore National Laboratory?
  • Included in 2026Best Places to Work by Glassdoor!
  • Flexible Benefits Package
  • 401(k)
  • Relocation Assistance
  • Education Reimbursement Program
  • Flexible schedules (*depending on project needs)
  • Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance
None required.However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check.
National Defense Authorization Act (NDAA)
The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.
Pre-Employment Drug Test
External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Wireless and Medical Devices
Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.
How to identify fake job advertisements
Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.
To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf
Equal Employment Opportunity
We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
Reasonable Accommodation
Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.
CaliforniaPrivacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here .

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