... physics-informed neural networks to solve real-world engineering problems • Data driven analysis of complex systems • Experience inferring root cause from sparse measurements • Demonstrable ...
... physics-informed neural networks to solve real-world engineering problems • Data driven analysis of complex systems • Experience inferring root cause from sparse measurements • Demonstrable ...
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Applied Scientist - Computational Modeling, OMHS SCS
Boston, MA · On-site
$136 - $184/hr
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
Applied Scientist - Computational Modeling, OMHS SCS
Boston, MA · On-site
$136 - $184/hr
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
ECE Tenure-Track Faculty Position in Physical AI
Blacksburg, VA · On-site
$13.25 - $17.50/hr
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
ECE Tenure-Track Faculty Position in Physical AI
Blacksburg, VA · On-site
$13.25 - $17.50/hr
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Preferred : • Dynamical systems modeling • Experience with lumped parameter modeling of physical systems • Experience implementing physics-informed neural networks to solve real-world ...
Preferred : • Dynamical systems modeling • Experience with lumped parameter modeling of physical systems • Experience implementing physics-informed neural networks to solve real-world ...
Staff Deep Learning Engineer
$185K - $235K/yr
Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data. * Track record of taking a research idea from paper to production-deployed model. What We ...
Staff Deep Learning Engineer
$185K - $235K/yr
Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data. * Track record of taking a research idea from paper to production-deployed model. What We ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
Senior Staff Machine Learning Engineer, Tapestry
Mountain View, CA · On-site
$123K - $169K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
Senior Staff Machine Learning Engineer, Tapestry
Mountain View, CA · On-site
$123K - $169K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
Preferred : • Dynamical systems modeling • Experience with lumped parameter modeling of physical systems • Experience implementing physics-informed neural networks to solve real-world ...
Preferred : • Dynamical systems modeling • Experience with lumped parameter modeling of physical systems • Experience implementing physics-informed neural networks to solve real-world ...
Senior Staff Machine Learning Engineer
Mountain View, CA · On-site +1
$144K - $190K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
Senior Staff Machine Learning Engineer
Mountain View, CA · On-site +1
$144K - $190K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
$184 - $288/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks).* Background with NVIDIA Omniverse, OpenUSD ...
$184 - $288/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks).* Background with NVIDIA Omniverse, OpenUSD ...
Senior AI Solutions Architect - Industrial Engineering
Santa Clara, CA · On-site
$65 - $83.75/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks). * Experience with NVIDIA Omniverse, OpenUSD ...
Senior AI Solutions Architect - Industrial Engineering
Santa Clara, CA · On-site
$65 - $83.75/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks). * Experience with NVIDIA Omniverse, OpenUSD ...
Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets
Oak Ridge, TN · On-site
... physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc. * Design, develop, and ...
Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets
Oak Ridge, TN · On-site
... physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc. * Design, develop, and ...
Senior Staff Machine Learning Engineer
Mountain View, CA · On-site
$144K - $190K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
Senior Staff Machine Learning Engineer
Mountain View, CA · On-site
$144K - $190K/yr
Advance the application of state-of-the-art AI architectures-including physics-informed neural networks and agentic AI-to solve highly constrained energy-infrastructure challenges in production ...
Senior AI Solutions Architect - Industrial Engineering
California, MO · On-site
$184 - $288/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks). * Background with NVIDIA Omniverse, OpenUSD ...
Senior AI Solutions Architect - Industrial Engineering
California, MO · On-site
$184 - $288/hr
Experience GPU-accelerating CFD/FEA solvers, or developing physics-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics-informed neural networks). * Background with NVIDIA Omniverse, OpenUSD ...
Senior Staff Machine Learning Engineer, Tapestry
Mountain View, CA · On-site
$262 - $361/hr
Advance the application of state-of-the-art AI architectures--including physics-informed neural networks and agentic AI to solve highly constrained energy-infrastructure challenges in production ...
Senior Staff Machine Learning Engineer, Tapestry
Mountain View, CA · On-site
$262 - $361/hr
Advance the application of state-of-the-art AI architectures--including physics-informed neural networks and agentic AI to solve highly constrained energy-infrastructure challenges in production ...
Machine Learning Engineer
Austin, TX · On-site
$170K - $250K/yr
Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series). * Incorporate physics-informed constraints so ...
Machine Learning Engineer
Austin, TX · On-site
$170K - $250K/yr
Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series). * Incorporate physics-informed constraints so ...
Experience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs). * Background in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing ...
New
Experience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs). * Background in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing ...
New
Machine Learning Engineer
Fremont, CA · On-site
$170 - $250/hr
Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series). * Incorporate physics-informed constraints so ...
New
Machine Learning Engineer
Fremont, CA · On-site
$170 - $250/hr
Design and train surrogate models (neural networks, Gaussian processes, gradient-boosted trees, GNNs/PINNs) on Azure GPU compute (ND/NC series). * Incorporate physics-informed constraints so ...
New
Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets
Oak Ridge, TN · On-site
... physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc. * Design, develop, and ...
Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets
Oak Ridge, TN · On-site
... physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc. * Design, develop, and ...
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 Aug 24, 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 network?
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 does a physics informed neural network do?
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.
What are the key skills and qualifications needed to thrive in physics informed neural networks?
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 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:
Full-time
Re-posted 10 days ago
SpaceX rating
8.7
Based on 150 frontline employees who took The Breakroom Quiz
16th of 72 rated aerospace companies
Job description
Job Summary:
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. In this role, the Sr. Software Engineer for Propulsion Simulation & Data Analysis will be responsible for maintaining and improving simulation models for the Raptor engine, conducting analysis to support engine design, and collaborating with engineers to enhance operational tools and processes.
Responsibilities:
• Maintain and improve the accuracy of the lumped-parameter thermofluid physics model of the Raptor engine
• Conduce engine cycle analysis to steer future-state engine design and thrust upgrades
• Conduct anomaly resolution utilizing the engine model and sparse instrumentation
• Develop a detailed understanding of the physics underlying rocket engine performance, sources of performance variability, as well as related modeling and measurement techniques
• Maintain and improve the accuracy of the representation of the Raptor engine used in integrated vehicle simulations, control software validation and flight Monte Carlo analysis
• Develop tools and portals to understand the sources of engine performance variability and enact improvements to manufacturing and engine hardware design
• Automate processes and software tools that are used to predict test performance and generate flight performance predictions for GNC (guidance, navigation, and control) and flight software
• Collaborate with engineers across the company to extend and improve interoperability among existing tools as well as to standardize hardware and software practices, tools, and methods to maximize functionality and value while minimizing future overhead and obsolescence
Qualifications:
Required:
• Bachelor's degree in computer science, engineering, or a STEM discipline
• 5+ year experience writing computer software
• 5+ year experience with physics-based simulations of real dynamic systems
Preferred:
• Dynamical systems modeling
• Experience with lumped parameter modeling of physical systems
• Experience implementing physics-informed neural networks to solve real-world engineering problems
• Data driven analysis of complex systems
• Experience inferring root cause from sparse measurements
• Demonstrable proficiency in the basic principles of compressible and incompressible flow, thermodynamics, thermochemistry, mechanics, and materials
• Strong written and verbal communication skills, ability to make presentations to team members, internal customers and management
• Experience building data pipelines providing sanitization, curation, visualization, exploration to high dimensional datasets
• Experience working with or simulating combustion engines or fluid systems
• Full stack web development experience (database design, frontend)
• Frontend experience in Angular, React, or a similar JavaScript framework
• Backend Development: deliver well-tested, performant, and maintainable Python, C#/.NET, C++ or Java code
• Experience with version control, continuous integration, Unix-like operating systems
• Experience with control feedback loop design and implementation
• Experience with numerical analysis techniques and signal processing in multiple domains
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 was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. In this role, the Sr. Software Engineer for Propulsion Simulation & Data Analysis will be responsible for maintaining and improving simulation models for the Raptor engine, conducting analysis to support engine design, and collaborating with engineers to enhance operational tools and processes.
Responsibilities:
• Maintain and improve the accuracy of the lumped-parameter thermofluid physics model of the Raptor engine
• Conduce engine cycle analysis to steer future-state engine design and thrust upgrades
• Conduct anomaly resolution utilizing the engine model and sparse instrumentation
• Develop a detailed understanding of the physics underlying rocket engine performance, sources of performance variability, as well as related modeling and measurement techniques
• Maintain and improve the accuracy of the representation of the Raptor engine used in integrated vehicle simulations, control software validation and flight Monte Carlo analysis
• Develop tools and portals to understand the sources of engine performance variability and enact improvements to manufacturing and engine hardware design
• Automate processes and software tools that are used to predict test performance and generate flight performance predictions for GNC (guidance, navigation, and control) and flight software
• Collaborate with engineers across the company to extend and improve interoperability among existing tools as well as to standardize hardware and software practices, tools, and methods to maximize functionality and value while minimizing future overhead and obsolescence
Qualifications:
Required:
• Bachelor's degree in computer science, engineering, or a STEM discipline
• 5+ year experience writing computer software
• 5+ year experience with physics-based simulations of real dynamic systems
Preferred:
• Dynamical systems modeling
• Experience with lumped parameter modeling of physical systems
• Experience implementing physics-informed neural networks to solve real-world engineering problems
• Data driven analysis of complex systems
• Experience inferring root cause from sparse measurements
• Demonstrable proficiency in the basic principles of compressible and incompressible flow, thermodynamics, thermochemistry, mechanics, and materials
• Strong written and verbal communication skills, ability to make presentations to team members, internal customers and management
• Experience building data pipelines providing sanitization, curation, visualization, exploration to high dimensional datasets
• Experience working with or simulating combustion engines or fluid systems
• Full stack web development experience (database design, frontend)
• Frontend experience in Angular, React, or a similar JavaScript framework
• Backend Development: deliver well-tested, performant, and maintainable Python, C#/.NET, C++ or Java code
• Experience with version control, continuous integration, Unix-like operating systems
• Experience with control feedback loop design and implementation
• Experience with numerical analysis techniques and signal processing in multiple domains
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
Aerospace product and parts manufacturing, data services, guided missile and space vehicle manufacturing and satellite telecommunications
Company size
1,001 - 5,000 Employees
Headquarters location
Hawthorne, CA, US