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Physics Simulation Python Jobs in San Francisco, CA

1X

Software Engineer - Simulation

San Carlos, CA · On-site

Required : • 4+ years of experience programming in Python, C++, or similar languages, with ... Physics simulation depth understanding what makes a simulator physically accurate and ...

1X

Software Engineer - Simulation

San Carlos, CA · On-site

Required : • 4+ years of experience programming in Python, C++, or similar languages, with ... of physics simulators or OpenGL rendering pipelines • Strong testing practices for simulation ...

Skild AI

Simulation Engineer

San Mateo, CA

Proficiency in programming languages such as Python, C++, or similar. * Deep understanding of commonly used physics engine simulators for robotics. * Technical understanding of different components ...

Hammerhead AI

Simulation Engineer

Redwood City, CA · On-site

Proficiency in Python, Go and/or C++ and experience with simulation frameworks or libraries. * Physics-Based Modeling: Strong understanding of first-principles modeling, with experience capturing the ...

HammerheadAI

Simulation Engineer

Redwood City, CA · On-site

Proficiency in Python, Go and/or C++ and experience with simulation frameworks or libraries. * Physics-Based Modeling: Strong understanding of first-principles modeling, with experience capturing the ...

Luminary Cloud

Geometry/Meshing Engineer

San Mateo, CA · On-site

... for physics simulation (e.g., CFD, structural analysis, thermal simulation) and Physics AI ... Proficient in Python. Enterprise-level software development and testing practices. * Operates at ...

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Physics Simulation Python information

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$13K

$79.6K

$143.1K

How much do physics simulation python jobs pay per year?

As of Jul 29, 2026, the average yearly pay for physics simulation python in San Francisco, CA is $79,646.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,800.00 and $93,700.00 per year, depending on experience, location, and employer.

What is the difference between Physics Simulation Python vs Mechanical Engineer?

AspectPhysics Simulation PythonMechanical Engineer
Required CredentialsProgramming skills, knowledge of physics, often a degree in physics or computer scienceMechanical engineering degree, professional licensure in some regions
Work EnvironmentSoftware development, research labs, simulation environmentsDesign offices, manufacturing plants, R&D departments
Industry UsageSimulation software development, research, academiaProduct design, manufacturing, systems optimization

Physics Simulation Python focuses on developing and implementing physics-based simulations using Python programming, often in research or software development contexts. Mechanical Engineers apply engineering principles to design, analyze, and manufacture mechanical systems. While both roles require a strong understanding of physics, Physics Simulation Python emphasizes coding and simulation, whereas Mechanical Engineering involves practical design and application in physical systems.

Does NASA hire physicists?

Yes, NASA hires physicists for roles involving research, space science, and engineering projects. These positions often require advanced degrees in physics or related fields and familiarity with scientific tools and data analysis methods. Physicists at NASA contribute to mission development, data interpretation, and technological innovation.

What are the key skills and qualifications needed to thrive as a Physics Simulation Python Developer, and why are they important?

To excel as a Physics Simulation Python Developer, you need a strong background in physics, mathematics, and proficiency in Python programming, often supported by a degree in physics, engineering, or computer science. Familiarity with simulation libraries (such as NumPy, SciPy, PyBullet, or SimPy), version control systems like Git, and experience with visualization tools are commonly required. Analytical thinking, problem-solving abilities, and effective collaboration are standout soft skills in this role. These skills enable the development of accurate, efficient simulations and foster productive teamwork in research or engineering projects.

What are some common challenges faced by professionals working in Physics Simulation with Python, and how can they be addressed?

Professionals in Physics Simulation with Python often encounter challenges such as optimizing simulation performance, ensuring numerical accuracy, and integrating complex libraries (e.g., NumPy, SciPy, PyBullet) into larger workflows. Addressing these issues typically involves using efficient coding practices, leveraging vectorized operations, and validating results with analytical solutions or experimental data. Collaboration with domain experts and regular code reviews can also help maintain code reliability and project scalability. Staying updated with the latest simulation frameworks and actively participating in open-source communities are excellent ways to overcome technical hurdles.

Is Python still in demand in 2026?

Python remains highly in demand for physics simulation roles in 2026 due to its versatility, extensive libraries like NumPy and SciPy, and strong community support. Professionals skilled in Python, along with knowledge of scientific computing and simulation frameworks, are sought after in research, engineering, and development environments.

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 simulation modeling. They often work in fields such as aerospace, defense, energy, and software development, utilizing programming skills and scientific expertise to solve complex physical problems.

What is a Physics Simulation Python developer?

A Physics Simulation Python developer is a professional who uses the Python programming language to design, implement, and analyze simulations that model physical systems and phenomena. These simulations can range from simple particle motion to complex fluid dynamics or electromagnetic fields, and are widely used in research, engineering, gaming, and education. The developer typically utilizes scientific libraries such as NumPy, SciPy, and PyBullet, and may also work with visualization tools to present simulation results. Their work helps in understanding real-world physics problems, testing hypotheses, or creating realistic interactive environments.

Is Python good for physics simulation?

Physics simulation Python roles often require knowledge of Python libraries such as NumPy, SciPy, and PyBullet, which are well-suited for modeling physical systems. Python's ease of use, extensive scientific computing ecosystem, and ability to integrate with other tools make it a popular choice for developing and running physics simulations in research and industry. Proficiency in numerical methods and understanding of physics principles are also important for these positions.
What are popular job titles related to Physics Simulation Python jobs in San Francisco, CA? For Physics Simulation Python jobs in San Francisco, CA, the most frequently searched job titles are:
What job categories do people searching Physics Simulation Python jobs in San Francisco, CA look for? The top searched job categories for Physics Simulation Python jobs in San Francisco, CA are:
What cities near San Francisco, CA are hiring for Physics Simulation Python jobs? Cities near San Francisco, CA with the most Physics Simulation Python job openings:
Physics Simulation Python jobs near you
Infographic showing various Physics Simulation Python job openings in San Francisco, CA as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $79,646 per year, or $38.3 per hour.

Physics Simulation Scientist

Skild AI

San Mateo, CA

Other

Posted 19 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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