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Physics Informed Machine Learning Jobs in Stanford, CA

SirenOpt

Quality Manager

San Leandro, CA · On-site

Using a novel combination of cold atmospheric plasma, physics-informed machine learning, and predictive analytics, SirenOpt creates unique, real-time fingerprints that capture material signals no ...

SirenOpt

Lead Software Engineer

San Leandro, CA · On-site

Using a novel combination of cold atmospheric plasma, physics-informed machine learning, and predictive analytics, SirenOpt creates unique, real-time fingerprints that capture material signals no ...

Workday

Machine Learning Engineer III

Pleasanton, CA

Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math or equivalent * 3+ yrs experience as a member of a data science, machine learning engineering, or ...

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Physics Informed Machine Learning information

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$6

$23

$29

How much do physics informed machine learning jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for physics informed machine learning in Stanford, CA is $23.57, according to ZipRecruiter salary data. Most workers in this role earn between $14.66 and $29.95 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What job categories do people searching Physics Informed Machine Learning jobs in Stanford, CA look for? The top searched job categories for Physics Informed Machine Learning jobs in Stanford, CA are:
What cities near Stanford, CA are hiring for Physics Informed Machine Learning jobs? Cities near Stanford, CA with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Stanford, CA as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 28% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $49,032 per year, or $23.6 per hour.

Developer Relations Manager, Higher Education and Research - Physics

Nvidia

Santa Clara, CA • On-site

Full-time

Re-posted 4 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

We are seeking a dynamic, mission-driven Developer Relations Manager to engage leading research labs. In this role, you will work directly with research labs to accelerate the adoption of NVIDIA's sophisticated AI and computing platforms.

The ideal candidate combines deep, technical expertise with strong experience in research engagement and a proven track record of turning advanced technologies into meaningful, research-focused results.

What you'll be doing:

  • Act as a trusted technical advisor for research labs, identifying and accelerating high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects.

  • Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies

  • Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas.

  • Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement

  • Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted.

  • Attend domain scientific conferences and facilitate NVIDIA participation alongside other subject matter experts.

  • Represent the needs of researchers internally by translating academic feedback into actionable insights that shape product development, education programs, and platform roadmaps.

What we need to see:

  • PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, or a related technical field; or equivalent experience demonstrating comparable research depth.

  • 3+ years of experience.

  • Deep expertise applying AI to computational physics, particle physics, astrophysics, plasma/fusion, quantum systems, materials science, climate/earth systems, or scientific simulation.

  • Strong fluency in AI methods relevant to physics, including neural operators, surrogate models, physics-informed ML, differentiable simulation, simulation-based inference, generative modeling, and uncertainty quantification.

  • Understanding of physics research workflows, including HPC simulations, numerical solvers, experimental/sensor data, large-scale instruments, inverse problems, and GPU-accelerated scientific pipelines.

  • Ability to engage top physics and national lab researchers on physical consistency, interpretability, numerical accuracy, reproducibility, simulation speedups, and scientific validity.

  • Research credibility through publications, open-source scientific software, academic or national lab collaborations, technical leadership, or hands-on work in AI for physics or computational science.

Ways to stand out from the crowd:

  • Experience with NVIDIA technologies and platforms such as CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NIM, NeMo/Nemotron, PhysicsNeMo, cuQuantum, Omniverse, Isaac, RAPIDS, TensorRT, Triton Inference Server, or DGX/accelerated computing systems.

  • Established relationships with leading academic labs, research institutes, national labs, or major open source research communities.

  • Track record translating frontier research into demos, reference architectures, workshops, technical content, or developer enablement programs.

  • Experience presenting at academic conferences, research workshops, technical summits, or university facing events.

  • Ability to identify emerging research trends and convert them into strategic opportunities for collaboration, platform adoption, and ecosystem growth.

NVIDIA is widely considered to be one of the technology world's most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 11, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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