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Computational Physics Jobs in California (NOW HIRING)

Voltai

Computational Scientist

Palo Alto, CA · On-site

PhD in computational physics, applied mathematics, computational engineering, or a closely related field * Deep expertise in numerical PDE methods: FEM, FVM, or BEM -- weak formulations, quadrature ...

Toyon

Computational Multiphysics Engineer

Goleta, CA · On-site

$125K - $195K/yr

While a background in electromagnetics and antenna design is ideal, we also welcome candidates with strong expertise in computational physics, fluid mechanics, thermodynamics, or materials science ...

Nvidia

Senior Software Engineer - NVIDIA Warp

Santa Clara, CA · On-site

$143K - $189K/yr

Our team develops Warp for computational physics, AI, and optimization workflows. We are looking for a hands-on senior engineer to expand Warp's adoption across science and engineering. Our goal is ...

Nvidia

Senior Software Engineer - NVIDIA Warp

Santa Clara, CA · On-site

$143K - $189K/yr

Our team develops Warp for computational physics, AI, and optimization workflows. We are looking for a hands-on senior engineer to expand Warp's adoption across science and engineering. Our goal is ...

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Showing results 21-40

All JobsComputational Physics JobsComputational Physics Jobs in California

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.

Computational Scientist

Voltai

Palo Alto, CA • On-site

Full-time

Re-posted 16 days ago

Job description

About Voltai
Voltai is developing world models, and agents to learn, evaluate, plan, experiment, and interact with the physical world. We are starting out with understanding and building hardware; electronics systems and semiconductors where AI can design and create beyond human cognitive limits.

About the Team

Backed by Silicon Valley’s top investors, Stanford University, and CEOs/Presidents of Google, AMD, Broadcom, Marvell, etc. We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of Defense, National Security Advisor, and Senior Foreign-Policy Advisor to four US presidents.

What You'll Work On

  • Develop and scale MPI+CUDA PDE solvers for electrostatics, charge transport, and electromagnetic field problems on complex 3D IC geometries across multi-node GPU clusters

  • Tune and extend AMG preconditioners, Krylov solvers, and mesh pipelines for performance and correctness at scale

  • Build and train neural operators (FNO, DeepONet, GNO, and variants) as high-fidelity surrogates for PDE-based field solvers

  • Design simulation pipelines that generate training data for neural operator models — including sampling strategies, mesh handling, and physical consistency checks

  • Validate everything: analytical solutions, published benchmarks, and cross-validation between field solvers and learned surrogates

Required

  • PhD in computational physics, applied mathematics, computational engineering, or a closely related field

  • Deep expertise in numerical PDE methods: FEM, FVM, or BEM — weak formulations, quadrature, convergence, error analysis

  • Strong C++ and CUDA — writing and optimizing kernels, memory hierarchy, multi-GPU programming

  • Multi-node HPC: MPI, domain decomposition, collective communication, strong/weak scaling

  • Sparse linear algebra at depth: Krylov methods, algebraic multigrid, preconditioning strategies

  • Hands-on experience with neural operators (FNO, DeepONet, or equivalent) — training, architecture design, and evaluation on PDE datasets

  • Solid understanding of AI for Science methodology: how to design datasets from simulations, handle out-of-distribution generalization, and ensure physical consistency of learned models

Strongly Preferred

  • Experience with HYPRE, PETSc, and Trilinos

  • Familiarity with multi-node GPU clusters: NCCL, CUDA-aware MPI, NVLink topologies

  • Published work in neural operators, physics-informed ML, or scientific HPC

  • IC design domain knowledge: device physics, semiconductor materials, layout data formats