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Physics Informed Neural Networks Jobs (NOW HIRING)

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $195K/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 - $195K/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 - $195K/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 ...

Open Orion

Head of AI

San Francisco, CA · On-site

... physics-informed neural networks Preferred : • Hands-on experience with CFD or FEM solvers • Experience with geometry kernels or parametric CAD APIs • Background in differentiable simulation or ...

Open Orion

ML Engineer

San Francisco, CA · On-site

... or physics-informed neural networks • Experience with differentiable simulation or differentiable rendering • Hands-on experience with CFD, FEM, or mesh generation • Contributions to open ...

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

As of Aug 28, 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.

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Physics Informed Neural Networks jobs near you
Infographic showing various Physics Informed Neural Networks job openings in the United States as of August 2026, with employment types broken down into 38% Full Time, 61% Part Time, and 1% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Founding Engineer (ML Systems) - Godela

Godela (YC X25)

San Francisco, CA • On-site

Full-time

Re-posted 2 days ago

Job description

Job Summary:
Godela is a pioneering company focused on building the first Physics Foundation Model, an AI system designed to predict and simulate physical behavior. They are seeking a Founding ML Engineer to develop and productionize large-scale, physics-informed ML systems, aiming to revolutionize R&D for engineers.
Responsibilities:
• Developing scalable approaches to train high-accuracy, large-scale physics-informed models.
• Designing and testing new architectures for multi-physics and multi-scale modeling
• Optimizing training efficiency through distributed computing, GPU optimization, and performance tuning.
Qualifications:
Required:
• Strong Python + PyTorch (or JAX/TF) skills.
• Proven experience delivering ML systems into production
• Experience with multi-GPU / multi-node training
• Experience training and deploying large-scale ML models with GPU acceleration and distributed workloads.
• Hands-on AWS experience (compute, storage, IAM, cuda) or other cloud infra provider.
• Background in building or scaling training pipelines, APIs, or ML infra that supports real-world products.
Preferred:
• Exposure to engineering simulations (CFD, FEM, PDEs) or physics-informed ML (PINNs, neural operators).
• Familiarity with Docker/Kubernetes, CI/CD, Slurm, or other workload managers.
• Experience with Graph Neural Networks, Physics-Informed Neural Networks, Neural Operators, and Transformer architectures
• Experience working with messy, high-dimensional data.
Company:
AI-powered physics models that replace simulation. Founded in 2025, the company is headquartered in San Francisco, USA, with a team of 2-10 employees. The company is currently Early Stage.

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