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Physics Informed Neural Network Jobs (NOW HIRING)

Quidient

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

Design and train novel deep neural network architectures from scratch for a variety of ... Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.

Quidient

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

Design and train novel deep neural network architectures from scratch for a variety of ... Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.

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 · 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 · 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 ...

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

As of Sep 10, 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 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 researcher?

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 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.

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 do physics-informed neural networks do?

Physics-informed neural networks (PINNs) are models that incorporate physical laws and equations into their training process to solve complex scientific and engineering problems. They are used to simulate systems governed by differential equations, improve predictive accuracy, and reduce the need for large datasets, often requiring knowledge of programming and machine learning tools. PINNs are valuable in fields like fluid dynamics, material science, and climate modeling.

What other helpful pages are available for Physics Informed Neural Network?

Other pages related to Physics Informed Neural Network:

Infographic showing various Physics Informed Neural Network job openings in the United States as of September 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 87% In-person, and 13% Hybrid job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Staff Deep Learning Engineer

Columbia, MD • On-site

Quidient
Software Development • 11 - 50 employees
Learn more about Quidientexternal

$185K - $235K/yr

Full-time

Medical, Life, Retirement, PTO

Re-posted 23 days ago

Job description

Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world's great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything. Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First.
Overview
We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an applied-ML role - you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.
This is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.
What You'll Do
Research & Network Design
  • Design and train novel deep neural network architectures from scratch for a variety of reconstruction tasks - including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.
  • Implement state-of-the-art papers and adapt published architectures to Quidient's specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production.
  • Identify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.
  • Design and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.
Model Development
  • Run end-to-end experiments independently - from hypothesis through data preparation, training, evaluation, and iteration - with minimal supervision.
  • Stay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.
  • Contribute production-quality C++ and Python to integrate trained models into the reconstruction engine.
  • Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM
  • Drive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.
What You Bring
Must-Have Qualifications:
  • Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience.
  • 6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.
  • Deep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.
  • Ability to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.
  • Strong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.
  • Willingness to work on-site in Columbia, MD, in a hybrid capacity.
  • Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification
Nice-to-Have Qualifications:
  • Experience in fast-paced or startup environments.
  • Publications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).
  • Experience designing evaluation pipelines and experiment infrastructure for deep learning research.
  • 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 Offer
Compensation:
  • Salary Range: $185,000 - $235,000.
  • Annual bonus and equity as appropriate.
Benefits:
  • Health insurance
  • HSA
  • 401(k) with company match
  • Life & disability insurance
  • Paid holidays & generous PTO
  • Opportunities for bonuses, equity, and career growth
Equal Opportunity Employer Statement
Quidient is an Equal Opportunity Employer. Quidient will consider all qualified applicants without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other classification protected by applicable state, federal, or local laws.

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