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Physics Informed Machine Learning Jobs in Berkeley, CA

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

Strong Expertise in Machine Learning, Deep Learning, and OptimizationKnowledges of Finite Element Analysis and/or other numerical methods in computational physics and mechanicsProficiency in Python ...

Pravāh

AI Weather Scientist

San Francisco, CA · On-site

$150K - $250K/yr

... physics-informed ML to weather and climate forecasting. * Work at the intersection of physics-based modeling and machine learning: hybrid physics-ML approaches, learned parameterizations, and ...

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 ...

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 ...

MSCI

Machine Learning Researcher

San Francisco, CA

$144K - $187K/yr

Conduct research using machine learning methodologies that integrate financial theory with deep ... PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ...

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Physics Informed Machine Learning information

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$6

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

As of Jun 14, 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 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 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:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Berkeley, CA as of June 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 16% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $51,097 per year, or $24.6 per hour.

Machine Learning Engineer

Skild AI

San Mateo, CA • On-site

Other

Posted 5 days ago

Job description

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities
  • Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
  • Design and conduct experiments to train RL models and conduct real-world tests.
  • Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
  • Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
  • Analyze and interpret experimental results, iterating on model design to achieve desired performance.
  • Stay up-to-date with the latest research and advancements in reinforcement learning.
Preferred Qualifications
  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
  • Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
  • Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
  • Strong background in algorithms, data structures, and software engineering principles.
  • 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 reinforcement learning and robotic systems.

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