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Physics Based Machine Learning Jobs in San Ramon, CA

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 ... physics-constrained ML, or graph-based learning as demonstrated in software artifacts or ...

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 ... physics-constrained ML, or graph-based learning as demonstrated in software artifacts or ...

Adobe

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

... Python-based ML and Data Science frameworks, with proficiency in at least one deep learning ... applied physics, engineering, or a related quantitative field. Company : Adobe is a software ...

MSCI

Machine Learning Researcher

San Francisco, CA

$144K - $187K/yr

PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ... Our research-based data, analytics and indexes, supported by advanced technology, set standards for ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ... Our research-based data, analytics and indexes, supported by advanced technology, set standards for ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ... Our research-based data, analytics and indexes, supported by advanced technology, set standards for ...

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Physics Based Machine Learning information

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

As of Jul 10, 2026, the average hourly pay for physics based machine learning in San Ramon, CA is $22.42, according to ZipRecruiter salary data. Most workers in this role earn between $13.99 and $28.46 per hour, depending on experience, location, and employer.

What types of projects or problems does a Physics Based Machine Learning professional typically work on?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What is a Physics Based Machine Learning job?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What are the key skills and qualifications needed to thrive in the Physics Based Machine Learning position, and why are they important?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What are popular job titles related to Physics Based Machine Learning jobs in San Ramon, CA? For Physics Based Machine Learning jobs in San Ramon, CA, the most frequently searched job titles are:
What cities near San Ramon, CA are hiring for Physics Based Machine Learning jobs? Cities near San Ramon, CA with the most Physics Based Machine Learning job openings:
Physics Based Machine Learning jobs near you

Machine Learning Engineer, Reinforcement Learning

Skild AI

San Mateo, CA • On-site

Other

Re-posted 23 hours 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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