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Physics Informed Machine Learning Jobs in California

... Physics, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying machine learning techniques and driving product direction). Company

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

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What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What job categories do people searching Physics Informed Machine Learning jobs in California look for? The top searched job categories for Physics Informed Machine Learning jobs in California are:
What cities in California are hiring for Physics Informed Machine Learning jobs? Cities in California with the most Physics Informed Machine Learning job openings:
Infographic showing various Physics Informed Machine Learning job openings in California as of June 2026, with employment types broken down into 1% Locum Tenens, 84% Full Time, 11% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution.
Machine Learning Physics Graduate Student

Machine Learning Physics Graduate Student

LLNL

Livermore, CA • On-site

$6.7K - $8.2K/mo

Internship

Retirement

Posted 17 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 multiple openings for Machine Learning Graduate Student Interns to engage in practical research experience to further their educational goals. You will work on multidisciplinary projects, such as development of classical empirical and machine learning interatomic potentials, discovery of partial differential equations (PDEs), numerical solutions of partial differential equations to model material behavior at continuum scale and analysis of large atomic datasets. These positions are in in the Equation of State Materials Theory Group of the Physics Division of the Physical & Life Sciences Directorate.
This position requires full-time on-site presence due to the nature of the work.
You will
  • Develop parallel C/C++/Python codes to train, test and evolve (a) PDEs (for phase field and phase field crystal models) discovered from data, and (b) interatomic potentials developed from quantum simulations.
  • Explore the use of machine learning methods to discover and evolve PDEs for phase field and phase field crystal models.
  • Analyze results, provide weekly updates and present work at poster sessions
  • Review literature in the field of study, document results and write papers.
  • Perform other duties as assigned.

Qualifications
  • Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
  • Continuing student in good standing at an accredited institution of higher education pursuing a graduate degree in Physics or related field.
  • Research background with a record of publication.
  • Experience in writing codes (in C/C++ and Python) and a background in Materials Science/Engineering/Physics/Applied Mathematics.
  • Excellent skills in written and verbal communication, as well as teamwork.

Qualifications We Desire
  • Experience in parallel computing, porting codes to GPUs, experience in numerical solutions of partial differential equations.

Pay Range
$6,752 - $8,201 Monthly
This position is under a step structure. Please note that the step placement is determined by your most recent completed academic year.
Additional Information
#LI-Onsite
Why Lawrence Livermore National Laboratory?
  • Included in 2026 Best Places to Work by Glassdoor!
  • Holiday Pay
  • Sick leave accrual
  • Individual 401(k) contributions
  • 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 use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate 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.
California Privacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job 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.
Videos To Watch
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