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Physics Informed Neural Network Jobs (NOW HIRING)

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

Substrate

Research Scientist, AI

San Francisco, CA · On-site

$150K - $275K/yr

Implement surrogate models, physics-informed neural networks, or generative approaches for scientific problems * Develop data pipelines and frameworks for scientific machine learning across ...

Digital Harbor Foundation

Research Scientist

Baltimore, MD · On-site +1

$120K - $150K/yr

Experience with physics-informed neural networks, neural operators, Graph Neural Networks, or AI-accelerated FEM modeling * Familiarity with uncertainty quantification methods (e.g., ensembles ...

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How much do physics informed neural network jobs pay per hour?

As of May 31, 2026, the average hourly pay for physics informed neural network in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Physics-Informed Neural Network (PINN) Researcher, and why are they important?

To thrive as a Physics-Informed Neural Network (PINN) Researcher, you need a strong background in applied mathematics, physics, and deep learning, typically supported by an advanced degree in a related field. Proficiency with programming languages such as Python, machine learning libraries (e.g., TensorFlow or PyTorch), and experience with scientific computing tools are essential. Strong analytical thinking, problem-solving skills, and effective communication help researchers interpret results and collaborate with interdisciplinary teams. These skills and qualities are critical for developing accurate models that integrate physical laws with data-driven methods, advancing scientific discovery.

What are some common challenges faced when implementing Physics Informed Neural Networks (PINNs) in real-world projects?

Implementing PINNs often involves challenges such as integrating complex physical laws into neural network architectures and ensuring that the model accurately balances data-driven learning with physical constraints. Additionally, training can be computationally intensive, especially when dealing with high-dimensional or stiff differential equations. Collaboration with domain experts—such as physicists or engineers—is typically necessary to correctly formulate the governing equations and interpret results. Despite these challenges, working on PINNs provides opportunities to contribute to cutting-edge applications in engineering, climate modeling, and scientific computing.

What is a Physics Informed Neural Network?

A Physics Informed Neural Network (PINN) is a type of machine learning model that incorporates physical laws, typically expressed as partial differential equations, into the training process of neural networks. By embedding these physical constraints, PINNs can solve forward and inverse problems in engineering and science more accurately and efficiently, even with limited data. They are especially useful for modeling complex systems where traditional data-driven approaches might fail to generalize or respect fundamental physical principles.

What is the difference between Physics Informed Neural Network vs Data Scientist?

AspectPhysics Informed Neural NetworkData Scientist
Required credentialsBackground in machine learning, physics, or engineering; often advanced degreesStatistics, computer science, or related fields; often advanced degrees
Work environmentResearch labs, academia, or tech companies focusing on modeling physical systemsBusiness, tech firms, or consulting firms analyzing data for insights
Industry usageEngineering, scientific research, simulation modelingFinance, marketing, healthcare, tech
Common search intentUnderstanding specialized AI models for physical systemsAnalyzing data patterns and extracting insights

Physics Informed Neural Networks are specialized AI models integrating physical laws into machine learning, primarily used in scientific and engineering contexts. Data Scientists focus on analyzing data to inform business decisions across various industries. While both roles involve machine learning, their applications and environments differ significantly.

Infographic showing various Physics Informed Neural Network job openings in the United States as of May 2026, with employment types broken down into 8% Internship, 75% Full Time, and 17% Part Time. Highlights an 92% In-person, and 8% Hybrid job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
Software Engineer, Simulation

Software Engineer, Simulation

SpaceX

Hawthorne, CA • On-site

Full-time

Posted 29 days ago

SpaceX rating

8.7

Company rating: 8.7 out of 10

Based on 142 frontline employees who took The Breakroom Quiz

12th of 59 rated aerospace companies

Job description

Job Summary:
SpaceX is a company focused on developing technologies for human life on Mars. They are seeking a Software Engineer specializing in simulation to enhance their multiphysics simulation engine, which supports various manufacturing processes. The role involves software architecture, optimization, and collaboration with cross-functional teams to improve simulation capabilities.
Responsibilities:
• Own software architecture and quality of simulation software used across SpaceX.
• Develop and optimize next-generation software that integrates CFD tools, meshing software, and CAD platforms for multiphysics simulations; contribute to transitioning results viewing to web-based 3D tooling.
• Build advanced interfaces for viewing and interpreting simulation results.
• Troubleshoot and debug issues related to software integration, meshing quality, and simulation accuracy.
• Leverage machine learning and AI solutions—such as surrogate modeling and physics-informed neural networks—to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts.
• Collaborate with cross-functional teams to implement new features and automate processes.
• Conduct testing and validation of simulation results using industry-standard benchmarks.
Qualifications:
Required:
• Bachelor's degree in computer science, engineering, math, or STEM discipline; OR 5+ years of professional experience building software in lieu of a degree.
• 2+ years of software development experience.
• 2+ years of strong C++ software engineering experience.
• 1+ years of experience leveraging Python for data analysis.
• Ability to work extended hours and weekends as necessary.
• Ability to travel to other SpaceX sites as needed (up to 20%).
Preferred:
• Experience working with numerical solvers for complex physics domains (e.g., tools like OpenFOAM, ANSYS Fluent, COMSOL Multiphysics).
• Strong meshing skills for complex simulations (e.g., tools like NX, ANSYS Meshing, SnappyHexMesh).
• Demonstrated experience applying machine learning and AI solutions (e.g., surrogate modeling, physics-informed neural networks, Fourier neural operators, neural operators, graph neural networks etc) to accelerate simulations.
• Experience with visualization tools (ParaView, VisIt, Tecplot) and web-based 3D tooling (e.g., Three.js).
• Experience with web development frameworks such as Flask, SQLAlchemy, and FastAPI.
• Front-end experience in React or similar JavaScript UI frameworks.
• Database experience with PostgreSQL, SQL Server, or similar database technologies.
• Good understanding of version control, testing, continuous integration, build, deployment, and monitoring.
• Strong Linux experience.
• Knowledge of high-performance computing (HPC) environments and parallel processing.
• Strong problem-solving abilities and attention to detail for optimizing simulation efficiency.
• Excellent communication skills for documenting code and collaborating in team settings.
Company:
SpaceX designs, manufactures, and launches rockets and spacecraft to facilitate space exploration. Founded in 2002, the company is headquartered in Hawthorne, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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