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

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · Remote

By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Work with real-world structured and unstructured scientific data (e.g., experimental, simulation ...

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · On-site +1

By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Work with real-world structured and unstructured scientific data (e.g., experimental, simulation ...

NobleAI

Senior Applied AI/ML Scientist

Houston, TX

By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Work with real-world structured and unstructured scientific data (e.g., experimental, simulation ...

Raytheon

Sr Systems Engineer - Onsite

Houston, TX

$99.80K - $136.60K/yr

... operational analysis, simulation, mission planning and architecture development activities ... Experience with programming languages such as Python, MATLAB, and/or C++, and experience with both ...

Bluespark Energy

Pulsed Power Engineer

Houston, TX · On-site

Proficiency in simulation tools (SPICE, MATLAB, CST, PLECS, PSIM, COMSOL, or equivalent ... Python programming skills Behavioral Competencies * Growth mindset with a passion for emerging ...

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Physics Simulation Python information

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$10.5K

$64.6K

$116K

How much do physics simulation python jobs pay per year?

As of Jun 1, 2026, the average yearly pay for physics simulation python in Houston, TX is $64,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,000.00 and $75,900.00 per year, depending on experience, location, and employer.

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.

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 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 Houston, TX? For Physics Simulation Python jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Physics Simulation Python jobs in Houston, TX look for? The top searched job categories for Physics Simulation Python jobs in Houston, TX are:
What cities near Houston, TX are hiring for Physics Simulation Python jobs? Cities near Houston, TX with the most Physics Simulation Python job openings:
Physics Simulation Python jobs near you
Infographic showing various Physics Simulation Python job openings in Houston, TX as of May 2026, with employment types broken down into 14% Internship, 72% Full Time, and 14% Contract. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $64,557 per year, or $31 per hour.
Data Scientist, Reinforcement Learning

Data Scientist, Reinforcement Learning

ExxonMobil

Spring, TX • On-site

Part-time

Medical, Dental, Vision, Life, Retirement

Posted yesterday

ExxonMobil rating

6.1

Company rating: 6.1 out of 10

Based on 220 frontline employees who took The Breakroom Quiz

57th of 74 rated oil and gas companies

Job description

Your role on our team
Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across ExxonMobil's upstream, downstream, and commercial operations.
Collaborate with engineers, scientists, and business stakeholders to turn complex operational and planning problems into deployable, production-grade RL solutions.
Advance the organization's capabilities in reinforcement learning, decision optimization, and autonomous control as part of the Modeling, Optimization, and Data Science (MODS) team.
What you will do
  • Design, develop, and deploy reinforcement learning solutions for real-world energy applications such as production optimization, process control, supply chain scheduling, drilling optimization, and resource allocation.
  • Formulate sequential decision problems by defining state spaces, action spaces, reward structures, transition dynamics, and operational constraints with domain experts.
  • Develop RL agents using model-free methods (e.g., PPO, SAC, TD3, DQN where appropriate) and model-based approaches, selecting methods based on problem requirements, safety, and data availability.
  • Build and use simulation environments and digital twins for offline training, policy evaluation, and validation before real-world deployment.
  • Apply safe and constrained RL techniques to ensure agents operate within operational and safety limits.
  • Integrate RL solutions with existing optimization, simulation, and control systems across real-time and planning use cases.
  • Partner with data scientists and ML engineers to operationalize solutions, including training pipelines, monitoring, retraining, and performance tracking.
  • Benchmark RL against traditional methods such as LP, MIP, heuristic search, MPC, and stochastic optimization to identify best-fit approaches.
  • Stay current with advances in offline RL, safe RL, multi-agent RL, hierarchical RL, and model-based RL.
  • Share knowledge, publish findings where appropriate, and mentor peers on RL best practices.

About you
Desired Skills:
  • Experienced AI/ML professional with strong expertise in reinforcement learning, sequential decision-making, optimization, and real-world deployment.
  • 5+ years of experience in AI/ML, optimization, or related fields, including at least 2 years in reinforcement learning, sequential decision-making, or optimal control.
  • Master's or PhD in Computer Science, Machine Learning, Operations Research, Control Theory, Robotics, Applied Mathematics, Engineering, or a related quantitative field.
  • Deep understanding of RL fundamentals, including MDPs, dynamic programming, temporal-difference learning, policy gradients, and actor-critic methods.
  • Proven experience building RL systems end-to-end, from environment and reward design through training, evaluation, and deployment.
  • Experience with simulation environments, digital twins, or system models.
  • Strong background in statistics, probability, optimization, control theory, and algorithm design.
  • Proficiency in Python, PyTorch and/or TensorFlow, plus RL tools such as Stable Baselines3, RLlib, and Gymnasium.
  • Strong communication and collaboration skills, including the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills:
  • Experience applying RL or decision optimization in industrial domains such as process control, robotics, autonomous systems, supply chain, energy systems, or operations research.
  • Familiarity with offline (batch) RL, safe RL, and multi-agent RL.
  • Knowledge of model-based RL, MPC, and hybrid RL-control approaches.
  • Understanding of classical optimization methods and how RL complements them.
  • Experience with physics-informed or hybrid mechanistic/ML modeling and domain-informed reward or constraint design.
  • Familiarity with platforms such as Azure ML, Azure OpenAI, Databricks, and MLOps tools such as MLflow or Weights & Biases.
  • Experience in the energy industry or other asset-intensive, safety-critical sectors.

Your benefits
An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
We offer you:
  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life.
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match.
  • Workplace Flexibility: We have several programs such as "Flex your Day", providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans.
  • Culture of Health: Programs and resources to support your wellbeing.
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you.
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.

More information on our Company's benefits can be found at www.exxonmobilfamily.com.
Please note benefits may be changed from time to time without notice, subject to applicable law.
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