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Physics Informed Machine Learning Jobs in Berkeley, CA

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 ...

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$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 ...

MSCI

Machine Learning Researcher

San Francisco, CA

$144K - $187K/yr

MSCI is establishing a Machine Learning Center of Excellence within the Research & Development team ... PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ...

Workday

Machine Learning Engineer III

Pleasanton, CA · On-site

Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math or equivalent * 3+ yrs experience as a member of a data science, machine learning engineering, or ...

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

As of Jul 5, 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
Machine Learning Engineer

Machine Learning Engineer

Swish Analytics

San Francisco, CA • On-site, Remote

$160K/yr

Full-time

Posted 17 days ago

Job description

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients.
The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to "roll your own" and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.
This position is 100% remote
Responsibilities:
  • Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.
  • Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
  • Build, test, deploy and maintain production systems.
  • Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
  • Support maintenance and optimization of cloud-native EDW and ETL solutions.
  • Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.
  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
  • Use extensive experience to build, test, debug, and deploy production-grade components.
  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
  • Participate in development of database structures that fit into the overall architecture of Swish systems

Qualifications:
  • Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
  • 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs
  • A proven background in quantitative analytics, trading, or engineering is required for this position
  • Demonstrated experience developing data science modeling systems and infrastructure at scale
  • Experience with Python and exposure to modern machine learning frameworks
  • Proficient in SQL; experience with MySQL
  • Background and/or interest in Rust preferred
  • Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback
  • Strong communication skills when discussing technical concepts with technical and non-technical colleagues

Base salary: starting at $160,000 base plus bonus potential
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.
Department Engineering & Infrastructure Role Data Science Infrastructure Locations San Francisco, CA - Remote Remote status Fully Remote

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