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Physics Based Machine Learning Jobs in San Ramon, CA

Stand Insurance

Physics Team Lead

San Francisco, CA · On-site

$210K - $250K/yr

Experience integrating physics-based models with machine learning systems for acceleration or inference. Compensation: The annual base salary range for full-time employees in this position is $210 ...

PayPal

Sr Machine Learning Engineer

San Jose, CA · On-site

$143K - $189K/yr

Experience implementing and enhancing graph-based and relational machine learning techniques for structured or graph data (1 year) 14. Experience performing data preprocessing, feature engineering ...

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

$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 ...

PayPal, Inc.

Sr Machine Learning Engineer

San Jose, CA · On-site

$143K - $189K/yr

Experience implementing and enhancing graph-based and relational machine learning techniques for structured or graph data (1 year) 14. Experience performing data preprocessing, feature engineering ...

PayPal

Sr Machine Learning Engineer

San Jose, CA · On-site

$143K - $189K/yr

Experience implementing and enhancing graph-based and relational machine learning techniques for structured or graph data (1 year) 14. Experience performing data preprocessing, feature engineering ...

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

As of Jun 18, 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 job categories do people searching Physics Based Machine Learning jobs in San Ramon, CA look for? The top searched job categories for Physics Based Machine Learning jobs in San Ramon, CA 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
Infographic showing various Physics Based Machine Learning job openings in San Ramon, CA as of June 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $46,635 per year, or $22.4 per hour.

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA • On-site

Full-time

Posted 6 hours 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.

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