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Physics Based Machine Learning Jobs in San Ramon, CA

Experience in working with subject matter experts in one or more areas, such as physics, biology ... An employee's position within the salary range will be based on several factors including, but not ...

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Machine Learning FEA Engineer

San Francisco, CA · On-site

$150.40 - $277.60/hr

... based on predictive finite element simulations and important design load cases. The machine ... Knowledges of Finite Element Analysis and/or other numerical methods in computational physics and ...

... Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering ... Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of ...

New

... Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering ... Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of ...

New

... based device that can stimulate and image the brain at high resolution and depth. This is a ... Strong first-principles understanding of engineering, physics, and signal processing. * Experience ...

... based device that can stimulate and image the brain at high resolution and depth. This is a ... Strong first-principles understanding of engineering, physics, and signal processing. * Experience ...

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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 Aug 21, 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 is a physics based machine learning?

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 does a physics based machine learning professional do?

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 are the key skills and qualifications needed to thrive in physics based machine learning?

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 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:

Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule ...

Genentech

San Francisco, CA • On-site, Remote

$160K - $297K/yr

Full-time

Re-posted yesterday


Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

11th of 86 rated pharmaceutical


Job description

A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech's Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist building agents for applied small-molecule drug design. You will develop autonomous, LLM-driven agentic workflows that orchestrate ML models, physics-based methods, and cheminformatics tools to accelerate discovery, working with world-class chemists and structural biologists.

The Opportunity:

  • Design, build, and apply agentic workflows and ML models for key challenges in small-molecule drug design.

  • Fine-tune foundation models for drug discovery relevant topics using internal and external datasets and tools.

  • Optimize agent-derived hypotheses in close collaboration with world-class computational and medicinal chemists and structural biologists.

  • Drive scientific impact through publications, open-source releases, and conference talks.

  • Collaborate widely with computational and experimental researchers at Roche and with academic partners.

Who you are:

  • You are experienced developing LLM-driven agents for scientific workflows and you understand how to orchestrate tools and models reliably.

  • You bring strong machine-learning foundations in linear algebra, probability and optimization, with hands-on experience with GNNs, sequence/language models and reinforcement learning.

  • You are fluent in Python and modern agentic coding environments such as LangChain, ML frameworks such as PyTorch or JAX, as well as cheminformatics toolkits like RDKit or OpenEye.

  • You hold a PhD or equivalent research depth in machine learning, computer science, chemical engineering or a related quantitative field such as physics or statistics, with up to 2 years of industry research experience (Scientist) or 2+ years of industry research experience (Senior Scientist).

  • You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab)..

Preferred:

  • Hands-on experience orchestrating multi-tool or multi-agent scientific pipelines.

  • Hand-on experience working along the small molecule drug discovery value chain and an excitement to engage with chemists

  • Familiarity with structural biology datasets

If you want to put autonomous AI to work discovering the medicines patients need next, apply now and help build the future of drug design at Roche.

Relocation benefits are NOT available for this opportunity

The expected salary range for this position, based on the primary location of San Francisco, is $147,600 - $274,000 for the ML Scientist, and $167,400 - 310,800 for the Senior ML Scientist. For the primary of location of New York City, $141.100 - $262,100 for the ML Scientist, and $160,100 - 297,300 for the Senior ML Scientist. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#ComputationCoE

#tech4lifeComputationalScience

#tech4lifeAI

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.


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About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

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