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Physics Simulation Python Jobs in Austin, TX (NOW HIRING)

Koch Industries

Machine Learning Engineer

Austin, TX · On-site

$170K - $250K/yr

Strong Python with PyTorch or TensorFlow. * Understanding of relevant engineering/physics fundamentals and simulation data formats for your domain. * Experience with Azure Machine Learning or a ...

Raytheon

Senior Simulation Engineer Onsite

Austin, TX

$103K - $142K/yr

Experience using Python to analyze data, model behaviors, or support operational workflows ... Advanced degree in Physics, Applied Mathematics, Electrical Engineering, or Aerospace Engineering

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Physics Simulation Python information

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$10.9K

$67K

$120.4K

How much do physics simulation python jobs pay per year?

As of Aug 27, 2026, the average yearly pay for physics simulation python in Austin, TX is $67,007.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,600.00 and $78,800.00 per year, depending on experience, location, and employer.

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.

What are the key skills and qualifications needed to thrive as a physics simulation Python developer?

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.

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.

What are popular job titles related to Physics Simulation Python jobs in Austin, TX?

For Physics Simulation Python jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Physics Simulation Python jobs in Austin, TX look for?

The top searched job categories for Physics Simulation Python jobs in Austin, TX are:

What cities near Austin, TX are hiring for Physics Simulation Python jobs?

Cities near Austin, TX with the most Physics Simulation Python job openings:

Physics Simulation Python jobs near you
Infographic showing various Physics Simulation Python job openings in Austin, TX as of June 2026, with employment types broken down into 50% Full Time, 25% Part Time, and 25% Contract. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $67,007 per year, or $32.2 per hour.

Physicist with Python Proficiency - AI Trainer

Kake Group

Austin, TX • Remote

$51.50 - $71/hr

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

We're building a talent pool of Physics Experts with Python proficiency to contribute to project-based AI development initiatives, focused on evaluating and enhancing frontier AI models.

Designed for physics professionals who enjoy deep technical problem-solving, this pipeline role is for those looking to apply their simulation expertise to evaluate and push the boundaries of frontier AI models, relying on domain-specific simulation tools, such as FEniCS, OpenFOAM, Meep, REBOUND, or CAMB, with verifiable, code-graded answers run inside isolated Linux environments.

Key Responsibilities

  • Identify an appropriate physics simulation package and build problems whose solution genuinely hinges on that tool's core capabilities, whether PDE solvers, integrators, or Monte Carlo kernels.
  • Develop full Python solutions for each problem, providing all necessary input files, boundary conditions, and domain or initial condition definitions.
  • Establish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the physical context.
  • Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10% and 30%.
  • Tune solver parameters, field configurations, and initial conditions iteratively, building an understanding of how the model navigates complex simulation environments.
  • Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission.

Core Requirements

  • Academic background in Physics, Theoretical, Experimental, or Computational, or an equivalent field.
  • At least 2 years of hands-on experience in physics research, applied work, or teaching.
  • Solid Python skills, applied to writing and validating computational solutions.
  • Capacity to build problems that cannot be solved without specialized simulation software.
  • Excellent written and verbal communication skills in English.
  • Ability to work independently in a remote, fast-paced environment.

Nice-to-Have

  • Working knowledge of one or more domain-specific simulation tools, including but not limited to: FEniCS/DOLFINx, OpenFOAM, Meep, MPB, openEMS, Geant4, PYTHIA8, ROOT/PyROOT, WarpX, REBOUND, MESA, CAMB, CLASS, or Bilby, or a demonstrated ability to get up to speed independently.
  • Prior exposure to how frontier AI models approach complex simulation tasks.
  • Knowledge spanning more than one physics domain, such as fluid dynamics, electromagnetism, gravitation, or cosmology.
  • Familiarity with containerized or sandboxed Linux execution environments.

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.