... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield, reduce inspection burden, and optimize parts. • Collaborate with ...
Software Engineer, Simulation
$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 ...
Software Engineer, Simulation
$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 ...
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 ...
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 ...
Software Engineer, Simulation
$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 ...
Software Engineer, Simulation
$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 ...
Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning
Tulsa, OK · On-site
$85K - $108K/yr
Physics-Informed Neural Networks (PINNs) * Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) * Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) * Operator ...
Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning
Tulsa, OK · On-site
$85K - $108K/yr
Physics-Informed Neural Networks (PINNs) * Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) * Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) * Operator ...
Neural-network representations of controllers and estimators (e.g., Physics-Informed Neural Networks for MPC, or Neural Network based MPC) * Control-oriented modeling of physical systems, both from ...
Neural-network representations of controllers and estimators (e.g., Physics-Informed Neural Networks for MPC, or Neural Network based MPC) * Control-oriented modeling of physical systems, both from ...
Physics-Informed Neural Networks (PINNs) Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) Operator learning ...
Physics-Informed Neural Networks (PINNs) Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) Operator learning ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Quick apply
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Neural-network representations of controllers and estimators (e.g., Physics-Informed Neural Networks for MPC, or Neural Network based MPC) * Control-oriented modeling of physical systems, both from ...
Neural-network representations of controllers and estimators (e.g., Physics-Informed Neural Networks for MPC, or Neural Network based MPC) * Control-oriented modeling of physical systems, both from ...
ML Engineer, Surrogate Modeling (Vehicle Engineering)
Hawthorne, CA · On-site
$145K - $175K/yr
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
ML Engineer, Surrogate Modeling (Vehicle Engineering)
Hawthorne, CA · On-site
$145K - $175K/yr
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
ML Engineer, Surrogate Modeling (Vehicle Engineering)
Hawthorne, CA · On-site
$145K - $175K/yr
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
ML Engineer, Surrogate Modeling (Vehicle Engineering)
Hawthorne, CA · On-site
$145K - $175K/yr
Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural ...
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 ...
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 ...
Research Scientist, AI
$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 ...
Research Scientist, AI
$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 ...
Physics Informed Neural Networks information
See salary details
$5.29 - $7.12
0% of jobs
$7.12 - $8.96
0% of jobs
$8.96 - $10.80
0% of jobs
$10.80 - $12.63
24% of jobs
$12.72 is the 25th percentile. Wages below this are outliers.
$12.63 - $14.47
16% of jobs
$14.47 - $16.30
0% of jobs
$16.30 - $18.14
0% of jobs
$18.14 - $19.97
0% of jobs
$19.97 - $21.81
0% of jobs
The median wage is $22.25 / hr.
$21.81 - $23.65
40% of jobs
$23.65 - $25.48
19% of jobs
$5
$20
$25
How much do physics informed neural networks jobs pay per hour?
As of Jun 23, 2026, the average hourly pay for physics informed neural networks 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 is a Physics Informed Neural Networks job?
A Physics Informed Neural Networks (PINNs) job typically involves developing and applying neural networks that incorporate physical laws as constraints to solve complex scientific and engineering problems. Professionals in this field work on integrating differential equations into deep learning models to improve predictions and reduce the need for large training datasets. These roles are common in fields like fluid dynamics, material science, and climate modeling, where traditional computational methods can be expensive. Individuals in this role often have expertise in machine learning, numerical methods, and domain-specific physics.
What are the key skills and qualifications needed to thrive in the Physics Informed Neural Networks position, and why are they important?
To thrive in Physics Informed Neural Networks (PINNs), you need a strong background in physics, mathematics, and deep learning frameworks, typically evidenced by advanced degrees in physics, applied mathematics, computer science, or engineering. Experience with programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch, as well as experience in numerical simulation tools, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help professionals excel in multidisciplinary teams. These qualifications and soft skills are essential for developing accurate, interpretable models that integrate scientific knowledge with machine learning to solve complex real-world problems.
What are the typical daily tasks involved in a Physics Informed Neural Networks position?
In a Physics Informed Neural Networks role, your daily tasks will often include designing, building, and testing neural network architectures that incorporate physical laws and constraints. You will frequently collaborate with domain experts, such as physicists or engineers, to integrate scientific knowledge into machine learning models and validate the results with real-world data. Regular responsibilities also involve coding, running experiments, analyzing results, and documenting findings for presentation or publication. This collaborative and research-driven environment helps ensure that models are both accurate and physically consistent, and offers opportunities for interdisciplinary learning and skill advancement.
More about Physics Informed Neural Networks jobs
What cities are hiring for Physics Informed Neural Networks jobs? Cities with the most Physics Informed Neural Networks job openings:
What states have the most Physics Informed Neural Networks jobs? States with the most job openings for Physics Informed Neural Networks jobs include:
What job categories do people searching Physics Informed Neural Networks jobs look for? The top searched job categories for Physics Informed Neural Networks jobs are:
SpaceX rating
8.7
Based on 144 frontline employees who took The Breakroom Quiz
13th of 60 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 develops and operates rockets, satellite networks, and AI infrastructure including launch, connectivity, and cloud services. Founded in 2002, the company is headquartered in Hawthorne, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
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 develops and operates rockets, satellite networks, and AI infrastructure including launch, connectivity, and cloud services. Founded in 2002, the company is headquartered in Hawthorne, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About SpaceX
Sourced by ZipRecruiter
Industry
Accounting services
Company size
1,001 - 5,000 Employees
Headquarters location
Hawthorne, CA, US
Year founded
2002