ZipRecruiter

Log In

1

Physics Informed Machine Learning Jobs in Stanford, CA

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site +1

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ... A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI ...

LLNL

AI/ML Postdoctoral Researcher

Livermore, CA · On-site

$123K/yr

... Physics and Artificial Intelligence/Machine Learning (AI/ML) to make immediate, high-impact ... and physics-informed neural networks for laser physics modeling. * Solve abstract and complex ...

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

Experience with physics simulation engines and tools for training RL. * Deep understanding of state-of-the-art machine learning techniques and models. * Extensive industry experience with ...

Apple

Machine Learning FEA Engineer

San Francisco, CA · On-site

Minimum Qualifications Strong Expertise in Machine Learning, Deep Learning, and Optimization Knowledges of Finite Element Analysis and/or other numerical methods in computational physics and ...

Applied Materials

Algorithm Developer III

Santa Clara, CA · On-site

$161K - $221K/yr

These solutions will leverage a hybrid approach that combines first-principles physics modeling with advanced machine learning techniques, including physics-informed neural networks (PINNs), to ...

Amat

Algorithm Developer III

Santa Clara, CA · On-site +1

$161K - $221K/yr

These solutions will leverage a hybrid approach that combines first-principles physics modeling with advanced machine learning techniques, including physics-informed neural networks (PINNs), to ...

NobleAI

Senior Applied AI/ML Scientist

San Francisco, CA · On-site +1

By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Strong familiarity with molecule generation, chemical foundation models, and/or physics-informed ML

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$146K - $222K/yr

We have an opening for Machine Learning Research experts to join our team and advance the ... Experience in working with subject matter experts in one or more areas, such as physics, biology ...

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$146K - $222K/yr

We have an opening for Machine Learning Research experts to join our team and advance the ... Experience in working with subject matter experts in one or more areas, such as physics, biology ...

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$9.8K/wk

We have an opening for Machine Learning Research experts to join our team and advance the ... Experience in working with subject matter experts in one or more areas, such as physics, biology ...

next page

Showing results 1-20

All JobsPhysics Informed Machine Learning JobsPhysics Informed Machine Learning Jobs in Stanford, CA

Physics Informed Machine Learning information

See Stanford, CA salary details

$6

$23

$29

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

As of Jul 20, 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 job?

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 are popular job titles related to Physics Informed Machine Learning jobs in Stanford, CA? For Physics Informed Machine Learning jobs in Stanford, CA, the most frequently searched job titles are:
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 July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $49,032 per year, or $23.6 per hour.

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA • On-site

Other

Re-posted 2 days ago

Job description

Who are we?

RZR Global is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.

Role Overview

We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.

Key Responsibilities

  • Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.

  • Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.

  • Analyze the impact of integrating new data sources and features into our models.

  • Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.

  • Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.

  • Document experiments, assumptions, and outcomes; maintain reproducibility

Required Skills / Experience

  • Bachelor's or Master's degree in Mathematics, Physics, Computer Science, or a related technical field.

  • At least 1 year of professional experience in machine learning, statistical analysis, and data analysis.

  • Experience with machine learning techniques such as regression, classification, and clustering.

  • Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).

  • Strong grasp of probability, statistics, and data analysis principles.

  • Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.

Nice-to-Have

  • Familiarity with system programming languages including C++ and Rust is a plus.

  • Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)

  • Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.

Apply