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Computational Physics Jobs in California (NOW HIRING)

Atria Group LLC

Need Data Scientist

San Diego, CA · On-site

MS in Engineering Mathematics, Statistics, Theoretical/Computational Physics, or related field * Solid knowledge of statistical techniques is required * Hands-on programming experience with one or ...

Periodic Labs

Research Scientist, Data

Menlo Park, CA · On-site

$250K - $350K/yr

Research experience in areas such as materials science, solid state chemistry, chemistry, computational physics, semiconductors Mechanics Minimum education: Bachelor's degree or similar experience ...

ATEA

Maintenance Technician

Westlake Village, CA · On-site

$65K - $80K/yr

Candidates with basic computer aptitude and strong communication skills are preferred About ATEA Tech: We leverage the predictive capability of our high-fidelity computational physics solvers ...

ATEA

Maintenance Technician

Westlake Village, CA · On-site

$65K - $80K/yr

... computational physics solvers, indigenous massively parallel supercomputer system, prototyping plant, and ballistics and mechanics lab to investigate a variety of high-rate physics phenomena. The ...

Novartis

Computational Medicinal Chemist

San Diego, CA · On-site

$138K - $257K/yr

... computational scientist like you to join our ranks. Imagine the opportunity to unlock hidden ... Take a leading role in cross-disciplinary mechanistic studies using physics-based modeling and ...

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Computational Physics information

See California salary details

$143.1K

$169.5K

$193.3K

How much do computational physics jobs pay per year?

As of Sep 4, 2026, the average yearly pay for computational physics in California is $169,539.00, according to ZipRecruiter salary data. Most workers in this role earn between $156,100.00 and $182,700.00 per year, depending on experience, location, and employer.

What is computational physics?

Computational physics is a branch of physics that uses computational methods and algorithms to solve complex physical problems that are difficult or impossible to address analytically. It combines physics, computer science, and applied mathematics to simulate physical systems, analyze data, and predict the behavior of matter and energy. Computational physicists often develop and use software to model phenomena such as quantum mechanics, fluid dynamics, material properties, and astrophysics. This field is essential for advancing scientific research in areas where experiments are too costly, dangerous, or impractical.

What are the key skills and qualifications needed to thrive as a computational physicist, and why are they important?

To thrive as a Computational Physicist, you need a solid background in physics, advanced mathematics, and computer science, typically supported by a relevant degree (such as a PhD or MSc). Proficiency in programming languages like Python, C++, or Fortran, as well as experience with simulation software and high-performance computing, is essential. Strong problem-solving abilities, attention to detail, and effective communication skills help you collaborate and present complex findings clearly. These skills enable accurate modeling, efficient data analysis, and successful teamwork on complex scientific projects.

What are some common challenges faced by computational physicists when working on interdisciplinary projects?

Computational physicists often collaborate with researchers from fields like engineering, chemistry, or biology, which can introduce challenges related to differing terminologies, methodologies, and priorities. Adapting complex physics models to suit the needs and constraints of other disciplines may require significant adjustments and clear communication. Additionally, integrating diverse data types and software tools can be technically demanding, but overcoming these challenges helps foster innovation and leads to broader scientific impact.

What is the difference between Computational Physics vs Data Scientist?

AspectComputational PhysicsData Scientist
Required CredentialsPhysics degree, computational skills, programmingStatistics, programming, data analysis
Work EnvironmentResearch labs, academia, scientific institutionsTech companies, finance, healthcare
Industry UsageScientific research, simulations, modelingBusiness insights, predictive analytics
Common Search/ComparisonComputational Physics vs Data Scientist

Computational Physics focuses on applying computational methods to solve physical problems, often in research or academia. Data Scientists analyze large datasets to extract insights across various industries. While both roles require programming skills, their applications and work environments differ significantly.

Is computational physics in demand?

Computational physics is in demand across industries such as research, aerospace, finance, and technology, where modeling and simulation are essential. Professionals with strong programming skills in languages like Python, C++, or Fortran and experience with high-performance computing are highly sought after. The field offers opportunities in academia, government labs, and private sector companies focused on scientific and technological innovation.

What can you do with a computational physics degree?

A computational physics degree prepares individuals for roles in research, data analysis, simulation development, and modeling across industries such as aerospace, energy, finance, and technology. Graduates often work as physicists, data scientists, software developers, or in technical consulting, utilizing programming skills and scientific knowledge to solve complex problems. Advanced positions may require additional specialization or experience with tools like Python, C++, or MATLAB.

Who hires computational physicists?

Computational physicists are hired by research institutions, government laboratories, universities, and private industry companies involved in scientific research, technology development, and data analysis. They often work in environments that require strong programming skills and knowledge of physics, using tools like simulation software and high-performance computing systems.

What are the most commonly searched types of Computational Physics jobs in California?

The most popular types of Computational Physics jobs in California are:

What are popular job titles related to Computational Physics jobs in California?

For Computational Physics jobs in California, the most frequently searched job titles are:

What job categories do people searching Computational Physics jobs in California look for?

The top searched job categories for Computational Physics jobs in California are:

What cities in California are hiring for Computational Physics jobs?

Cities in California with the most Computational Physics job openings:

Computational Physics jobs near you
Infographic showing various Computational Physics job openings in California as of August 2026, with employment types broken down into 5% Internship, 69% Full Time, 21% Part Time, and 5% Contract. Highlights an 84% In-person, and 16% Remote job distribution, with an average salary of $169,539 per year, or $81.5 per hour.

Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)

Midjourney

San Francisco, CA • On-site

Full-time

Posted 23 days ago

Job description

What you'll do
  1. Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy.
  2. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements.
  3. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks.
  4. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation.
  5. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes.
What we're looking for
  • Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains.
  • Ability to move fluidly between research prototypes and production-quality systems.
  • Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility.
  • A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.)
Useful experience
  • ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints.
  • Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts.
  • A background in computational physics or scientific computing.
  • Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging.
  • Experience in Agentic-SciML is a plus.
  • Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines.
  • Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

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