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Physics Based Machine Learning Jobs in Dublin, CA

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ...

... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...

We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ... A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI ...

... based, multi-task, hierarchical, multi-agent, etc.). * Strong background in algorithms, data ... Experience with physics simulation engines and tools for training RL. * Deep understanding of ...

... based on predictive finite element simulations and important design load cases. The machine ... in computational physics and mechanics Proficiency in Python and relevant packages for ML ...

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Physics Based Machine Learning information

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How much do physics based machine learning jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for physics based machine learning in Dublin, CA is $22.59, according to ZipRecruiter salary data. Most workers in this role earn between $14.09 and $28.70 per hour, depending on experience, location, and employer.

What is a physics based machine learning?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What does a physics based machine learning professional do?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What are the key skills and qualifications needed to thrive in physics based machine learning?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What job categories do people searching Physics Based Machine Learning jobs in Dublin, CA look for?

The top searched job categories for Physics Based Machine Learning jobs in Dublin, CA are:

What cities near Dublin, CA are hiring for Physics Based Machine Learning jobs?

Cities near Dublin, CA with the most Physics Based Machine Learning job openings:

Infographic showing various Physics Based Machine Learning job openings in Dublin, CA as of June 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $46,997 per year, or $22.6 per hour.

Machine Learning Physics Graduate Student

Livermore, CA • On-site

LLNL
Clean Energy Services • 5 - 10K employees

$6.7K - $8.2K/mo

Full-time

Retirement

Re-posted 27 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.