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Physics Informed Neural Networks Jobs in Washington, DC

Quidient

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

  • Medical

  • Life

  • Retirement

  • PTO

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 ...

Aitopics

Featured Feat. Data Scientist

Washington, DC · On-site

$80 - $157/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Electromagnetism | Mechanics | NumPy | Numerical Methods | Numerical Simulation * C# | Computational physics | MATLAB | NumPy | Numerical Simulation * Computer Vision | Convolutional Neural Networks ...

Quidient

Future Opportunities

Columbia, MD · On-site +1

  • Medical

  • Life

  • Retirement

  • PTO

Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs ... AI/ML, Deep Learning, and Neural Rendering * Software Engineering (C++, C#, Python, JavaScript, or ...

Venturegloballng

Senior Engineer, Production

Arlington, VA · Hybrid

$158K - $186K/yr

Using appropriate methodologies such data driven, physics-based and data analytics models depending ... neural networks) * Some basic programming skills is a plus (C++, Python etc.) * High level of ...

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

As of Aug 17, 2026, the average hourly pay for physics informed neural networks in Washington, DC is $22.72, according to ZipRecruiter salary data. Most workers in this role earn between $14.13 and $28.85 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 are popular job titles related to Physics Informed Neural Networks jobs in Washington, DC?

For Physics Informed Neural Networks jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Physics Informed Neural Networks jobs in Washington, DC look for?

The top searched job categories for Physics Informed Neural Networks jobs in Washington, DC are:

Physics Informed Neural Networks jobs near you
Infographic showing various Physics Informed Neural Networks job openings in Washington, DC as of August 2026, with employment types broken down into 44% Full Time, 54% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $47,264 per year, or $22.7 per hour.

Staff Deep Learning Engineer

Quidient

Columbia, MD • On-site

$185K - $235K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 29 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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