... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
... based machine learning architectures * Stay up to date with advancements in AI, LLMs, RAG ... Bachelor's (Master's or PhD preferred) degree in engineering, data/computer science, physics, math ...
Machine Learning Team Lead
San Francisco, CA · On-site
$250K - $295K/yr
Experience combining physics-based modeling and machine learning , including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches * Ability to operate across ...
Machine Learning Team Lead
San Francisco, CA · On-site
$250K - $295K/yr
Experience combining physics-based modeling and machine learning , including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches * Ability to operate across ...
Computer Science, Computational Biology, Machine Learning, Statistics, Mathematics, Physics), preferably with a thesis on a computer vision-related topic. • Previous industrial experience of deep ...
Computer Science, Computational Biology, Machine Learning, Statistics, Mathematics, Physics), preferably with a thesis on a computer vision-related topic. • Previous industrial experience of deep ...
By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ...
By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ...
Machine Learning Engineer
San Francisco, CA · On-site +1
We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ... A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI ...
Machine Learning Engineer
San Francisco, CA · On-site +1
We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San ... A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI ...
Machine Learning Engineer
San Mateo, CA · On-site
... based, multi-task, hierarchical, multi-agent, etc.). * Strong background in algorithms, data ... Experience with physics simulation engines and tools for training RL. * Deep understanding of ...
Machine Learning Engineer
San Mateo, CA · On-site
... based, multi-task, hierarchical, multi-agent, etc.). * Strong background in algorithms, data ... Experience with physics simulation engines and tools for training RL. * Deep understanding of ...
... based on predictive finite element simulations and important design load cases. The machine ... in computational physics and mechanics Proficiency in Python and relevant packages for ML ...
... based on predictive finite element simulations and important design load cases. The machine ... in computational physics and mechanics Proficiency in Python and relevant packages for ML ...
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 ...
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 ...
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 ...
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 ...
Scientist, Machine Learning
South San Francisco, CA · On-site
$170K - $220K/yr
Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field. * 4+ years of experience developing machine learning methods for scientific ...
Scientist, Machine Learning
South San Francisco, CA · On-site
$170K - $220K/yr
Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field. * 4+ years of experience developing machine learning methods for scientific ...
... based, multi-task, hierarchical, multi-agent, etc.). * Strong background in algorithms, data ... Experience with physics simulation engines and tools for training RL. * Deep understanding of ...
... based, multi-task, hierarchical, multi-agent, etc.). * Strong background in algorithms, data ... Experience with physics simulation engines and tools for training RL. * Deep understanding of ...
Scientist, Machine Learning
South San Francisco, CA · On-site
$170K - $220K/yr
Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field. * 4+ years of experience developing machine learning methods for scientific ...
Scientist, Machine Learning
South San Francisco, CA · On-site
$170K - $220K/yr
Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field. * 4+ years of experience developing machine learning methods for scientific ...
Required : • Proven experience in applying machine learning and AI techniques to solve real-world problems, particularly with generative AI and LLMs • Strong proficiency with Python-based machine ...
Required : • Proven experience in applying machine learning and AI techniques to solve real-world problems, particularly with generative AI and LLMs • Strong proficiency with Python-based machine ...
Integrate physics-based and ML + data-driven approaches , combining force field methods, quantum ... PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics ...
Integrate physics-based and ML + data-driven approaches , combining force field methods, quantum ... PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule ...
San Francisco, CA · On-site +1
$160K - $297K/yr
... 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 ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule ...
San Francisco, CA · On-site +1
$160K - $297K/yr
... 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 ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecul[...]
San Francisco, CA · On-site
$147.60 - $274/hr
... 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 ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecul[...]
San Francisco, CA · On-site
$147.60 - $274/hr
... 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 ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule Dru
San Francisco, CA · On-site
$160K - $297K/yr
... 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 ...
Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule Dru
San Francisco, CA · On-site
$160K - $297K/yr
... 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 ...
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 ...
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 ...
Physics Based Machine Learning information
See Alameda, CA salary details
$5.99 - $8.07
0% of jobs
$8.07 - $10.15
0% of jobs
$10.15 - $12.24
0% of jobs
$12.24 - $14.32
24% of jobs
$14.41 is the 25th percentile. Wages below this are outliers.
$14.32 - $16.40
16% of jobs
$16.40 - $18.48
0% of jobs
$18.48 - $20.56
0% of jobs
$20.56 - $22.64
0% of jobs
$22.64 - $24.72
0% of jobs
The median wage is $25.21 / hr.
$24.72 - $26.80
40% of jobs
$26.80 - $28.88
19% of jobs
$5
$22
$28
How much do physics based machine learning jobs pay per hour?
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 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 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.

Workday rating
7.6
Based on 12 frontline employees who took The Breakroom Quiz
153rd of 242 rated software companies
Job description
We're obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we're shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you'll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We're in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you'll do meaningful work with Workmates who've got your back. In return, we'll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you've found a match in Workday, and we hope to be a match for you too.
About the Team
Do you want to build AI-powered software that impacts millions of people every day? The AI Core team, part of Workday's AI Platform organization, tackles challenging problems at the intersection of machine learning, agentic reasoning, and enterprise-scale systems. Our work delivers critical AI platform capabilities and differentiated, deep-value agent applications.
About the Role
As a Machine Learning Engineer on the AI Core team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate with other engineers to deliver ML solutions across Workday's product ecosystem and use current software and data engineering stacks to enable training, deployment, and lifecycle management of a variety of ML models; supervised and unsupervised. Additionally, you will develop and deploy new APIs/services using Docker/Kubernetes at scale and leverage Workday's vast computing resources on rich datasets to deliver transformative value to our customers. Sound like your kind of challenge?
You are a strong technical leader with deep Python expertise and solid software engineering skills, capable of writing beautiful, well-designed code while delivering solutions efficiently. Specifically, you will:
- Own exploration, design and implementation of features for our sophisticated ML platforms, pipelines and services.
- Be responsible for evaluation, scalability and observability of these features.
- Apply machine learning techniques including LLMs and natural language understanding to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud-based machine learning architectures
- Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
- Serve as a technical role model for more junior engineers
About You
Basic Qualifications:
- 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 other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
- 3+ years of professional experience with Python and supporting numeric libraries, with experience in shipping production code and models
- 3+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)
Other Qualifications:
- 3+ years of professional experience in building information retrieval systems and/or graph-based recommendation systems.
- 3+ years of hands-on professional experience in developing large language models (LLMs), text generation models, or graph-based machine learning models for production, including data processing, model fine-tuning, model deployment and model evaluation
- 3+ years of professional experience building services to host machine learning models in production at scale
- 3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as PySpark, Pytorch, TensorFlow, and Sklearn
- 3+ years of professional experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker
- Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
- Professional experience in independently solving ambiguous, open-ended problems and technically leading team
Workday Pay Transparency Statement
The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate's compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday's comprehensive benefits, please click here.
Primary Location: USA.CA.Pleasanton
Primary Location Base Pay Range: $160,000 USD - $240,000 USD
Additional US Location(s) Base Pay Range: $136,200 USD - $240,000 USD
Our Approach to Flexible Work
With Flex Work, we're combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
At Workday, we are committed to providing an accessible and inclusive hiring experience where all candidates can fully demonstrate their skills. If you require assistance or an accommodation at any point, please email accommodations@workday.com.
Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!
At Workday, we value our candidates' privacy and data security. Workday will never ask candidates to apply to jobs through websites that are not Workday Careers.
Please be aware of sites that may ask for you to input your data in connection with a job posting that appears to be from Workday but is not.
In addition, Workday will never ask candidates to pay a recruiting fee, or pay for consulting or coaching services, in order to apply for a job at Workday.
About Workday
Sourced by ZipRecruiter
Workday's journey began with a transformative idea generated during a breakfast conversation between its founders in sunny California. What set us apart from the start was our people-centric culture, driven by the core value of prioritizing our employees. At Workday, the happiness, growth, and contributions of every team member are at the heart of who we are. Our collaborative and employee-focused culture is the key ingredient for our business success. We not only care for our people but also for the communities and the environment, all while maintaining profitability. Embrace your uniqueness, as we encourage our Workmates to shine brightly in their authentic selves. Our passion and energy make us distinct, and we are inspired to create a brighter workday for everyone.
Industry
Software development
Company size
10,000+ Employees
Headquarters location
Pleasanton, CA, US
Year founded
2005