ZipRecruiter

Log In

1

Physics Informed Neural Networks Jobs (NOW HIRING)

Everstar Inc

Founding AI Engineer

New York, NY · On-site

Experience with physics-informed neural networks , scientific computing, or simulation acceleration * Published research in ML/AI, contributions to open-source ML frameworks * Deep familiarity with ...

LLNL

AI/ML Postdoctoral Researcher

Livermore, CA · On-site

$123K/yr

... informed neural networks for laser physics modeling. * Solve abstract and complex problems, using in-depth analysis, and drawing from advanced level technical knowledge, best practices, and both ...

LLNL

AI/ML Postdoctoral Researcher

Livermore, CA · On-site

$123K/yr

... informed neural networks for laser physics modeling. * Solve abstract and complex problems, using in-depth analysis, and drawing from advanced level technical knowledge, best practices, and both ...

LLNL

AI/ML Postdoctoral Researcher

Livermore, CA · On-site

$9.8K/wk

... informed neural networks for laser physics modeling. * Solve abstract and complex problems, using in-depth analysis, and drawing from advanced level technical knowledge, best practices, and both ...

SpaceX

Sr. Software Engineer, Computer Vision

Hawthorne, CA · On-site

$124K - $163K/yr

... physics-informed neural networks (PINNs) or surrogate modeling Company : SpaceX develops and operates rockets, satellite networks, and AI infrastructure including launch, connectivity, and cloud ...

Heven AeroTech

AI/ML Research Scientist

Sterling, VA · On-site

Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...

Heven AeroTech

AI/ML Research Scientist

Sterling, VA · On-site

Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...

Heven AeroTech

AI/ML Research Scientist

Sterling, VA · On-site

Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...

next page

Showing results 1-20

All JobsPhysics Informed Neural Networks Jobs

Physics Informed Neural Networks information

See salary details

$5

$20

$25

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

As of Jul 15, 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:
Physics Informed Neural Networks jobs near you
Infographic showing various Physics Informed Neural Networks job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 14% Part Time, and 1% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
Senior/Principal Forward Deployed Engineer - Applied AI/ML

Senior/Principal Forward Deployed Engineer - Applied AI/ML

Luminary Cloud

San Mateo, CA • On-site

$142K - $197K/yr

Full-time

Posted 8 days ago

Job description

Full-time position | San Mateo, CA (Onsite)
JOIN THE REVOLUTION IN ENGINEERING INNOVATION
Luminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.
YOUR IMPACT
As a Senior/Principal Applied AI/ML Scientist on Luminary's Applied AI/ML team, you build the Physics AI models that power customer outcomes. You work in a matrix structure inside customer value delivery teams alongside a Lead Delivery Engineer, Applications Engineers, and Data & Platform engineers. You take real customer engineering problems, design and train the right model architectures, and partner with the team to deploy those models into production engineering workflows. You operate at the boundary of cutting-edge research and applied delivery - staying connected to the frontier of physics-informed ML while making sure your work ships and gets used.
WHAT YOU'LL DO
  • Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground-truth simulation.
  • Work with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.
  • Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
  • Collaborate with Luminary Research to apply state-of-the-art techniques - neural operators, diffusion models, geometric deep learning, latent representations - to real customer problems.
  • Embrace co-engineering: work side-by-side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.
  • Bring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.
  • Mentor junior team members and contribute to internal best practices for applied physics-informed ML.

WHAT YOU BRING
  • 5-10 years of experience in applied machine learning, with significant exposure to scientific computing, engineering simulation, or physics-informed ML. Principal-level candidates trend toward the upper end of the range.
  • Strong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production-quality ML code, not just research notebooks.
  • Hands-on experience training and deploying models on engineering or scientific data - surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.
  • Working knowledge of engineering simulation: CFD, FEA, EM, thermal, or related - enough to collaborate effectively with domain experts and understand what the model needs to learn.
  • Experience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).
  • Strong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.
  • Customer-facing presence; comfortable explaining model architectures and limitations to engineering audiences.
  • Self-starter mentality, persistent through iteration, willing to travel occasionally to customer sites.

PREFERRED QUALIFICATIONS
  • Advanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.
  • Published work in physics-informed ML, neural operators, scientific machine learning, or related fields.
  • Experience with physics-informed AI/ML frameworks (e.g. PhysicsNeMo, JAX-based scientific ML stacks) or foundation model fine-tuning pipelines for scientific data.
  • Prior experience in applied research roles at engineering, simulation, or scientific computing companies.
  • Track record of shipping models into production engineering workflows.

Apply