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Physics Informed Neural Network Jobs (NOW HIRING)

Heven AeroTech

AI/ML Research Scientist

Sterling, VA

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

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 ...

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

SpaceX

Sr. Software Engineer, Computer Vision

Hawthorne, CA · On-site

$160K - $225K/yr

Experience applying AI to physics or simulation domains, using physics-informed neural networks (PINNs) or surrogate modeling ADDITIONAL REQUIREMENTS: * Ability to work extended hours and weekends as ...

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

As of Jun 1, 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 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.

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 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.

Infographic showing various Physics Informed Neural Network job openings in the United States as of May 2026, with employment types broken down into 8% Internship, 75% Full Time, and 17% Part Time. Highlights an 92% In-person, and 8% Hybrid job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
Scientist I, Quantitative Systems Pharmacologist

Scientist I, Quantitative Systems Pharmacologist

Revolution Medicines

Redwood City, CA • On-site

Full-time

Posted 22 days ago

Job description

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company's R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company's RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.
The Opportunity:
We are seeking a QSP modeling & simulation scientist to be part of the Nonclinical Development and Clinical Pharmacology (NDCP) organization. This position will be responsible for developing, validating, and executing modeling projects with a focus on mechanistic PBPK-QSP mathematical models for small molecule programs to increase mechanistic understanding of compound PK behavior and drug distribution, pharmacological effects on RAS targets, support clinical translation, and drive future discovery and development efforts. As a Quantitative Systems Pharmacologist, you will:
  • Develop, validate, execute, and refine quantitative systems pharmacology (QSP) models, minimal physiologically based pharmacokinetic (PBPK) models, semi-mechanistic PK/PD models, and tumor growth models to support development and discovery phase projects including next-generation inhibitor design and assessment of combination potential.
  • Propose and perform in silico simulations to answer complex mechanistic questions, create data visualizations to effectively communicate modeling results to a wide-ranging audience, and devise strategies to improve model outputs.
  • Survey the related literature to understand key physiological and biological processes, abstract the basic mechanistic elements, identify the relevant data, and summarize assumptions to be incorporated into existing or new PBPK-QSP models.
  • Propose new mechanistic in vitro and in vivo experiments to test model assumptions and structure. Provide in silico support for preclinical translation including clinical efficacious doses/exposure projection, potential combination dosing regimens with other cancer therapeutics.
  • Work collaboratively with other functions to build internal infrastructure supporting data transfer and quality control.
  • Document contributions, including assumptions, mathematical models, data analyses, and data visualizations, to be shared with other scientists or used for archival purposes.

Required Skills, Experience and Education:
  • A Ph.D. in a quantitative discipline (systems pharmacology, computational biology, engineering, mathematics, physics, etc.) and 0-2 years of industry experience is desired.
  • Strong understanding of the principles and limitations of mathematical modeling, pharmacokinetic models, pharmacodynamic models, and quantitative systems pharmacology/biology models.
  • Proficiency in mathematical and computational methods including ordinary differential equations (ODEs), nonlinear systems, statistics, optimization, and parameter inference.
  • Proven record developing, calibrating, and validating dynamical system models in pharmacological and biological systems.
  • Demonstrable hands-on experience with programming languages used in scientific computing, such as MATLAB, Python, Julia, and R.
  • Capable of working proactively and independently to deliver high-quality modeling results in a timely manner.
  • Able to effectively communicate modeling assumptions, limitations, and simulation results to non-specialist and specialist audiences.
  • A critical thinker and team player who can work cross-functionally with others.

Preferred Skills:
  • Experience with diverse dynamical system methods like ODE-based, PDE-based, nonlinear mixed effects, agent-based, Markov, Boolean, etc.
  • Experience with integrating large data sets into QSP.
  • Experience with agentic coding workflows such as Copilot, Cursor, Codex, and Claude Code.
  • Experience with data-driven methods such as ML-based predictive regression models and physics-informed neural network models.
  • Experience with modeling software such as SimBiology, NONMEM, Pheonix WinNonlin, Monolix, Simcyp designer, etc.
    #LI-Hybrid #LI-CT1

The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range for an individual working onsite in Redwood City and will be adjusted for the local market a candidate is based in. Our base pay salary ranges are determined by role, level, and location. Individual base pay salary is determined by multiple factors, including job-related skills, experience, market dynamics, and relevant education or training.
Please note that base pay salary range is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities.
Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status.
Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy. For additional information, please contact privacy@revmed.com.
Base Pay Salary Range
$119,000-$149,000 USD
We are aware of recent recruitment scams in which individuals or organizations falsely represent themselves as being affiliated with Revolution Medicines. These scams may appear as false job advertisements or unsolicited contacts through communication or chat platforms, email, phone, or text message.
Please note that Revolution Medicines does not extend unsolicited employment offers and will never ask candidates to provide financial information, purchase equipment, or pay fees as part of the hiring process. All legitimate communication from Revolution Medicines will come from an official @revmed.com email address.
If you believe you've been contacted by someone impersonating a Revolution Medicines recruiter, please report it to careers@revmed.com so we can share these impersonations with our IT team for tracking and awareness.

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