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Physics Informed Machine Learning Jobs in Berkeley, CA

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 ...

... machine learning with engineering design, creating intelligent solutions for geometry and physics ... physics-informed neural networks Preferred : • Hands-on experience with CFD or FEM solvers • ...

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

Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of ...

Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of ...

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 ...

About the role As a Machine Learning Lead at Nudge, you will drive the development of next ... Strong first-principles understanding of engineering, physics, and signal processing. * Experience ...

Showing results 21-40

Physics Informed Machine Learning information

See Berkeley, CA salary details

$6

$24

$31

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

As of Sep 6, 2026, the average hourly pay for physics informed machine learning in Berkeley, CA is $24.57, according to ZipRecruiter salary data. Most workers in this role earn between $15.29 and $31.20 per hour, depending on experience, location, and employer.

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 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 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 popular job titles related to Physics Informed Machine Learning jobs in Berkeley, CA?

For Physics Informed Machine Learning jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in Berkeley, CA look for?

The top searched job categories for Physics Informed Machine Learning jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Physics Informed Machine Learning jobs?

Cities near Berkeley, CA with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Berkeley, CA as of August 2026, with employment types broken down into 6% Internship, 44% Full Time, 43% Part Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $51,097 per year, or $24.6 per hour.

Machine Learning FEA Engineer

Apple

San Francisco, CA • On-site

Full-time

This job post has expired 2 days ago. Applications are no longer accepted.


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 683 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

Imagine what you can do here! We are committed to pushing the boundaries of innovation and engineering excellence in product designs through machine learning and FEA simulations. We truly believe in the power of predictive simulation to make the impossible possible, transform industries and improve people's lives. As a member of the Product Design FEA team, you will play a pivotal role in developing innovative machine learning technologies and directly impact the success of new iPhone, iPad, Mac, Apple Watch, Vision Pro and many more future products. Come join us and put a dent in the universe!
Description
As a core member of the product design team, you will be responsible for developing and implementing ground-breaking machine learning methods that are based on predictive finite element simulations and important design load cases. The machine learning models will drive rapid design iterations by assessing potential risks and optimizing design trade-offs. You will be fully integrated with the product design team from the earliest stages to engineer ground breaking products.
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 mechanics
Proficiency in Python and relevant packages for ML
Outstanding communication skills
Passion for creating innovative, high-quality products
Desire to work in a fast-paced environment with passion for creating cutting edge products
M.S. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline along with 3+ years of relevant experience
Preferred Qualifications
Strong expertise in GNNs, CNNs, and transformer-based architectures
Implement and optimize these models for large-scale datasets on scalable ML platforms
Ability to work independently in white space and deal with an incredible fast-paced environment
Excellent cross-functional collaboration and written and verbal communication skills
Ph.D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline
Publications in top journals or conferences

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976