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Physics Informed Machine Learning Jobs in Berkeley, CA

Substrate

Research Scientist, AI

San Francisco, CA · On-site

$150K - $275K/yr

Implement surrogate models, physics-informed neural networks, or generative approaches for scientific problems * Develop data pipelines and frameworks for scientific machine learning across ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · 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 ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site +1

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 ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site +1

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ... A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI ...

Skild AI

Machine Learning Engineer

San Mateo, CA

$100K - $300K/yr

Experience with physics simulation engines and tools for training RL. * Deep understanding of state-of-the-art machine learning techniques and models. * Extensive industry experience with ...

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Physics Informed Machine Learning information

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

As of Jun 14, 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
Infographic showing various Physics Informed Machine Learning job openings in Berkeley, CA as of June 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 16% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $51,097 per year, or $24.6 per hour.

Machine Learning Team Lead

Stand Insurance

San Francisco, CA

Other

Medical, Dental, Vision, Retirement

Posted 10 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 https://frontier.standinsurance.com/.
We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.
Leadership: 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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