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Physics Based Machine Learning Jobs (NOW HIRING)

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site

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 ...

PhysicsX

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 ...

PhysicsX

Machine Learning Engineer

New York, NY · On-site

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

Skild AI

Machine Learning Engineer

San Mateo, CA

... 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 ...

Apple

Machine Learning FEA Engineer

San Francisco, CA · On-site

... 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 ...

Bayer

Principal Machine Learning Scientist

Cambridge, MA

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... Experience with established, physics-based protein modelling methods like Molecular Dynamics and/or ...

Bayer

Principal Machine Learning Scientist

Cambridge, MA · On-site

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... Experience with established, physics-based protein modelling methods like Molecular Dynamics and/or ...

Bayer

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... Experience with established, physics-based protein modelling methods like Molecular Dynamics and/or ...

UNISSANT

Machine Learning Engineer

Ashburn, VA · On-site

Bachelor's Degree in Computer Science, Data Science, Electrical Engineering, Physics, or a related ... Additional technical certifications may be considered based on program requirements. Communication ...

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

As of Aug 6, 2026, the average hourly pay for physics based machine learning in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

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.

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Physics Based Machine Learning jobs near you
Infographic showing various Physics Based Machine Learning job openings in the United States as of August 2026, with employment types broken down into 79% Full Time, 7% Part Time, 7% Temporary, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Machine Learning Team Lead

Stand Insurance

San Francisco, CA • On-site

$250K - $295K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 3 days ago

Job description

Why Join Stand: At Stand, you'll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our Stand World Model.
We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.
Our leadership team includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies.
Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.
Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can't and operate with far less friction.
Role Summary:
As the MLE Team Lead on the Applied Science team, you will lead the Machine Learning Engineering sub-team as it develops and deploys Stand's flagship AI capabilities spanning physics-informed machine learning, digital twins, computer vision, and spatial intelligence. You will own the technical direction, planning, and execution of critical AI initiatives, ensuring they align with business priorities, ship on schedule, and deliver measurable outcomes.
This is a player-coach role, combining direct technical work and the leadership work around it: people management, project planning, cross-team coordination, and process. Reporting directly to the Chief Science Officer, you will own key projects yourself while ensuring the broader MLE team is operating effectively, growing, and delivering real impact. You are the person who looks around corners, sees what the business needs, and turns "the business needs X" into "the team builds Y."
You will partner across Applied Science and the business to transform research and emerging technologies into scalable systems that directly influence underwriting, pricing, mitigation, inspection, and customer decision-making.
Key initiatives include:
  • Advancing physics-informed, AI-driven solvers and surrogate architectures
  • Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities
  • Driving R&D programs through to validation, deployment, and business adoption
  • Building production-ready AI systems that accelerate, automate, and scale risk analytics

What You'll Do:
  • Lead the Machine Learning Engineering sub-team, defining priorities, coordinating execution, and unblocking the team to deliver on critical AI initiatives
  • Manage and grow the team, running 1-on-1s and growth conversations, giving direct and timely feedback, managing performance, and mentoring engineers as the team scales
  • Design, build, and deploy machine learning systems spanning physics-informed AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components
  • Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance
  • Extend state-of-the-art models and surrogate architectures to accelerate simulation and risk analytics workflows
  • Guide, support, and build scalable ML infrastructure, including data pipelines, training systems, evaluation frameworks, and production monitoring
  • Improve how the team works, creating process improvements and maintaining traceability
  • Drive cross-functional alignment, coordinating across Applied Science and the business and clearly communicating modeling decisions, tradeoffs, and status
  • Set a multi-year vision for the MLE team's impact and articulate how its work moves the business

Core Skills (Must-Haves):
  • Proficiency with modern ML tooling and infrastructure
  • Experience leading engineers and technical initiatives, delivering complex projects through others as well as through direct individual contribution
  • Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery of complex technical programs from concept through production
  • Experience combining physics-based modeling and machine learning, including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches
  • Ability to operate across disciplines, connecting technical development to business objectives and customer impact, and articulating those links to the team
  • Strong, succinct communication and the judgment to balance research depth, delivery timelines, and business impact
  • Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments where problems, interfaces, and priorities evolve

Nice to Haves:
  • Prior experience as a people manager, specifically in high-growth environments
  • Experience with computer vision, multimodal learning, or spatially-aware architectures
  • Familiarity with building agentic systems and LLM-powered workflows
  • Experience in startups or zero-to-one technology development
  • Knowledge of geospatial, remote sensing, or Earth observation datasets and systems

Compensation:
The annual base salary range for full-time employees in this position is $250,000 to $295,000 plus meaningful Equity Grant.
Compensation decisions are dependent on several factors including, but not limited to, an individual's qualifications, location where the role is to be performed, internal equity, and alignment with market data.
Benefits:
  • Above-market Health, Dental, and Vision coverage
  • Weekly lunch stipend
  • Flexible time off + holidays
  • 401(k) plan
  • Commuter benefits
  • PAT & MAT Leave
  • Short-Term and Long-Term Disability
  • Monthly team gatherings
  • In-office perks

Work Authorization
Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining.
Equal Opportunity Employment
Stand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.
We are committed to providing reasonable accommodations for qualified individuals. If you require assistance
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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