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Physics Informed Machine Learning Jobs in Santa Clara, CA

Cepton

R&D Machine Learning Research Scientist

San Jose, CA · On-site

$180K - $220K/yr

Preferred Qualifications · Relevant publications in machine learning, computer vision, robotics, autonomous systems, physics-informed learning, or related fields. · Hands-on experience with LiDAR ...

Skild AI

Machine Learning Engineer

San Mateo, CA

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 ...

Amat

Algorithm Developer III

Santa Clara, CA · On-site +1

$161K - $221K/yr

These solutions will leverage a hybrid approach that combines first-principles physics modeling with advanced machine learning techniques, including physics-informed neural networks (PINNs), to ...

Applied Materials

Algorithm Developer III

Santa Clara, CA · On-site

$161K - $221K/yr

These solutions will leverage a hybrid approach that combines first-principles physics modeling with advanced machine learning techniques, including physics-informed neural networks (PINNs), to ...

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

As of Aug 8, 2026, the average hourly pay for physics informed machine learning in Santa Clara, CA is $23.56, according to ZipRecruiter salary data. Most workers in this role earn between $14.66 and $29.90 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?

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 Santa Clara, CA? For Physics Informed Machine Learning jobs in Santa Clara, CA, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Santa Clara, CA look for? The top searched job categories for Physics Informed Machine Learning jobs in Santa Clara, CA are:
What cities near Santa Clara, CA are hiring for Physics Informed Machine Learning jobs? Cities near Santa Clara, 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 Santa Clara, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $49,010 per year, or $23.6 per hour.

Senior/Principal Forward Deployed Engineer - Applied AI/ML

Luminary Cloud

San Mateo, CA • On-site

$142K - $197K/yr

Full-time

Re-posted 2 days ago

Job description

Full-time position | San Mateo, CA (Onsite)
JOIN THE REVOLUTION IN ENGINEERING INNOVATION
Luminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.
YOUR IMPACT
As a Senior/Principal Applied AI/ML Scientist on Luminary's Applied AI/ML team, you build the Physics AI models that power customer outcomes. You work in a matrix structure inside customer value delivery teams alongside a Lead Delivery Engineer, Applications Engineers, and Data & Platform engineers. You take real customer engineering problems, design and train the right model architectures, and partner with the team to deploy those models into production engineering workflows. You operate at the boundary of cutting-edge research and applied delivery - staying connected to the frontier of physics-informed ML while making sure your work ships and gets used.
WHAT YOU'LL DO
  • Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground-truth simulation.
  • Work with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.
  • Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
  • Collaborate with Luminary Research to apply state-of-the-art techniques - neural operators, diffusion models, geometric deep learning, latent representations - to real customer problems.
  • Embrace co-engineering: work side-by-side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.
  • Bring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.
  • Mentor junior team members and contribute to internal best practices for applied physics-informed ML.

WHAT YOU BRING
  • 5-10 years of experience in applied machine learning, with significant exposure to scientific computing, engineering simulation, or physics-informed ML. Principal-level candidates trend toward the upper end of the range.
  • Strong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production-quality ML code, not just research notebooks.
  • Hands-on experience training and deploying models on engineering or scientific data - surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.
  • Working knowledge of engineering simulation: CFD, FEA, EM, thermal, or related - enough to collaborate effectively with domain experts and understand what the model needs to learn.
  • Experience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).
  • Strong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.
  • Customer-facing presence; comfortable explaining model architectures and limitations to engineering audiences.
  • Self-starter mentality, persistent through iteration, willing to travel occasionally to customer sites.

PREFERRED QUALIFICATIONS
  • Advanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.
  • Published work in physics-informed ML, neural operators, scientific machine learning, or related fields.
  • Experience with physics-informed AI/ML frameworks (e.g. PhysicsNeMo, JAX-based scientific ML stacks) or foundation model fine-tuning pipelines for scientific data.
  • Prior experience in applied research roles at engineering, simulation, or scientific computing companies.
  • Track record of shipping models into production engineering workflows.

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