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Physics Simulation Jobs in California (NOW HIRING)

Luminary Cloud

LLM Engineer

San Mateo, CA · On-site

Customize foundation models for Physics AI and physics simulation domain expertise through fine-tuning, prompt engineering, and evaluation frameworks * Deploy and scale LLM infrastructure : Build ...

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Physics Simulation information

See California salary details

$10.9K

$66.7K

$119.9K

How much do physics simulation jobs pay per year?

As of Jul 13, 2026, the average yearly pay for physics simulation in California is $66,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,400.00 and $78,500.00 per year, depending on experience, location, and employer.

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

To thrive in a Physics Simulation role, you need a solid background in physics, mathematics, and computer science, often supported by a relevant STEM degree. Proficiency in simulation software (such as MATLAB, ANSYS, or COMSOL), programming languages (like Python or C++), and familiarity with numerical modeling systems are typically required. Strong problem-solving skills, attention to detail, and effective collaboration and communication abilities are highly valued in this field. These competencies ensure accurate simulation results, efficient teamwork, and the ability to tackle complex real-world challenges in research or industry settings.

What are the typical daily tasks and responsibilities for someone working in Physics Simulation?

Professionals in Physics Simulation spend much of their day developing, testing, and refining computational models to represent physical phenomena, often collaborating with engineers and scientists to define simulation objectives. Responsibilities may include writing or optimizing code, analyzing simulation outputs, adjusting parameters to improve accuracy, and documenting methodologies and results. You might also participate in team meetings to review project milestones, discuss findings, and recommend design or process improvements based on your simulations. The role often involves both independent technical work and cross-disciplinary cooperation, making adaptability and clear communication important for success.

What is a Physics Simulation job?

A Physics Simulation job involves developing and implementing mathematical models to replicate real-world physical behaviors in software. Professionals in this field work with physics engines, numerical methods, and programming to simulate motion, collisions, fluid dynamics, or other physical phenomena. These simulations are used in industries such as gaming, film VFX, engineering, and scientific research to test and visualize complex systems. Strong skills in physics, programming (e.g., C++, Python), and computational techniques are essential for success in this role.

What are the most commonly searched types of Physics Simulation jobs in California? The most popular types of Physics Simulation jobs in California are:
What are popular job titles related to Physics Simulation jobs in California? For Physics Simulation jobs in California, the most frequently searched job titles are:
What job categories do people searching Physics Simulation jobs in California look for? The top searched job categories for Physics Simulation jobs in California are:
What cities in California are hiring for Physics Simulation jobs? Cities in California with the most Physics Simulation job openings:
Physics Simulation jobs near you
Infographic showing various Physics Simulation job openings in California as of July 2026, with employment types broken down into 79% Full Time, 19% Part Time, 1% Temporary, and 1% Contract. Highlights an 82% Physical, 1% Hybrid, and 17% Remote job distribution, with an average salary of $66,716 per year, or $32.1 per hour.

Physics Simulation Scientist

Skild AI

San Mateo, CA

Other

Posted 3 days ago

Job description

Position Overview

We are seeking a Physics Simulation Scientist to lead advancements in the simulation and physics-solving backbone behind Skild's robot foundation model training. You will collaborate with external experts in GPU-accelerated physics engines and work with our internal robotics and learning teams to build a next-generation, open-source simulation stack for robotics sim-to-real.

You'll partner closely with engineers scaling simulation scene generation and with ML researchers pushing the limits of sim-to-real transfer. The ideal candidate brings deep physics-simulation expertise plus hands-on experience implementing and optimizing algorithms on modern GPUs.

Responsibilities
  • Improve and develop new physics solvers and modeling methods for high-DoF, contact-rich robotics tasks.
  • Design and implement GPU-accelerated solvers with a focus on throughput, stability, and scalability.
  • Profile and optimize simulation performance on modern GPU hardware and distributed clusters.
  • Work with external collaborators and the open-source community to advance simulation for robotics.
  • Collaborate with scene-generation engineers to scale robotic experience across diverse real-world environments.
  • Partner with ML researchers to improve sim-to-real transfer through better physical modeling, calibration, and training regimes.
  • Contribute to the long-term technical direction of Skild's physical modeling and sim-to-real strategy.
Preferred Qualifications
  • MS or PhD in Physics, Robotics, Computer Science, Applied Math, Engineering, or a related field, or equivalent hands-on experience.
  • Strong track record working on physics engines or high-fidelity simulators for articulated rigid bodies; experience with deformables, fluids, or differentiable simulation is a plus.
  • Deep understanding of dynamics, contact modeling, constraint-based methods, and integrators, including accuracy-speed tradeoffs.
  • Expertise in CUDA and GPU programming with proven ability to optimize for scale.
  • Proficiency in C++ and Python, and experience building reliable systems used by other technical teams.
  • Familiarity with how modern ML/RL pipelines consume simulation (vectorized environments, domain randomization, large-scale rollouts).
  • Experience with real robot platforms and strong intuition for where simulation diverges from reality.
  • Publications, open-source contributions, or shipped systems in simulation, robotics, graphics, or numerical computing are a strong plus.

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