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Physics Informed Neural Network Jobs (NOW HIRING)

The Pennsylvania State University

... Informed Physics Invertible Neural Network (TIP-INN) framework. The core objective of this research is to advance physics-informed machine learning architectures to process complex, real-world ...

Voltai

Computational Scientist

Palo Alto, CA · On-site

Published work in neural operators, physics-informed ML, or scientific HPC * IC design domain knowledge: device physics, semiconductor materials, layout data formats

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Physics Informed Neural Network information

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

As of Jun 9, 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 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.

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 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.
Infographic showing various Physics Informed Neural Network job openings in the United States as of June 2026, with employment types broken down into 89% Full Time, and 11% Part Time. Highlights an 84% In-person, 5% Hybrid, and 11% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
AI/ML Scientist Lead Engineer

AI/ML Scientist Lead Engineer

Luminary Cloud, Inc.

San Mateo, CA

Other

Posted 23 days ago

Job description

Physics AI Leader

Luminary helps engineering companies be more competitive by getting to market faster, creating new, better products, and reducing development risk. We do this with our Physics AI platform, the fastest and easiest way to build and deploy models to understand and instantly predict physical reality with precision. Customers span industries from automotive and aerospace, to leading sporting equipment providers, including Otto Aviation, Joby Aviation, Piper Aircraft and Trek Bikes. Luminary is a Series B company and is headquartered in San Mateo, California.

The Role

We're looking for a visionary Physics AI leader to drive our vision for Physics AI. This role is a player-coach who will lead the Physics AI team at Luminary, while contributing concrete ideas and product architecture to drive the delivery of Physics AI foundation models. The role is responsible for driving how Luminary changes customer engineering design workflows forever

Responsibilities
  • Develop Physics-AI Tooling: Architect and implement high-performance tools for physics-informed workflows, similar in scope and capability to NVIDIA Modulus/Physics-ML (formerly Physics-Nemo), ensuring the delivery of models built off of synthetic data
  • Foundation Model Research: Lead the development of large-scale foundation models for the physical sciences, inspired by the collaborative, cross-domain approach of initiatives like Polymathic AI.
  • Architectural Innovation: Design and optimize specialized neural architectures for multi-scale physical systems, e.g. AB-UPT and related operator learning methods.
  • Physics-Informed Machine Learning (PIML): Embed physical constraints (conservation laws, symmetries, and PDEs) directly into the loss functions and inductive biases of deep learning models to ensure physical consistency and data efficiency.
  • Scalable Engineering: Collaborate with software engineers to deploy these models at scale within the Luminary Cloud platform, enabling real-time or near-real-time simulation for complex CFD/FEA problems.
  • Leadership: Drive the deliverables of the physics AI team each quarter contributing to the larger Luminary platform
Qualifications Required
  • Masters degree or higher in Computer Science, Mechanical Engineering, Aerospace Engineering, or related field
  • 5+ years of experience building production software or ML systems
  • Experience with Physics Nemo models such as Domino and GeoTransolver
  • Experience with Geometry processing, Meshing, and physics solvers a must
  • Familiarity with developing LLM-powered applications a plus
  • Strong proficiency in Python
  • Proficiency using coding agents such as Claude Code
  • Familiarity with Physics AI, CAE, or physics simulation domains a critical requirement
  • Experience with distributed ML applications a big plus
What we are not looking for
  • Not looking for a pure manager for this role
  • Not looking for someone who has no background in Physics

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