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Physics Informed Machine Learning Jobs in Santa Clara, CA

Apple

Machine Learning Engineer- GenAI

Cupertino, CA

$142K - $263K/yr

Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field. Pay & Benefits At ...

Apple

Machine Learning Engineer- GenAI

Cupertino, CA

$150K - $277K/yr

Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field. Pay & Benefits At ...

Adobe

Senior Machine Learning Engineer

San Jose, CA

$172K - $306K/yr

A degree in computer science, statistics, operations research, applied physics, engineering, or a ... machine learning. Together, we'll develop groundbreaking solutions that empower creatives around ...

Meta

Product Manager, Machine Learning

Menlo Park, CA · On-site

$173K - $241K/yr

We are looking for extremely entrepreneurial Product Managers with Machine Learning expertise who ... Physics, Applied Sciences, or a related field and 2+ years of experience in the following: • ...

Apple

Machine Learning Engineer- Gen AI

Cupertino, CA

$150K - $225K/yr

Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field. Pay & Benefits At ...

Voltai

Machine Learning Engineer

Palo Alto, CA · On-site

Strong AI/ML engineering skills from top tier CS, EECS, Math and Physics programs. * Proven track record of delivering AI/ML projects from concept to production. * Hands-on experience fine-tuning and ...

Voltai, Inc

Machine Learning Engineer

Palo Alto, CA · On-site

Strong AI/ML engineering skills from top tier CS, EECS, Math and Physics programs. * Proven track record of delivering AI/ML projects from concept to production. * Hands-on experience fine-tuning and ...

Voltai

Machine Learning Engineer

Palo Alto, CA · On-site

Strong AI/ML engineering skills from top tier CS, EECS, Math and Physics programs. * Proven track record of delivering AI/ML projects from concept to production. * Hands‑on experience fine‑tuning ...

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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 Aug 13, 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 67% Full Time, and 33% Part Time. Highlights an 100% In-person job distribution, with an average salary of $49,010 per year, or $23.6 per hour.

ML Research Scientist, Foundation Models (Senior / Staff / Principal)

Genesis Molecular AI

San Mateo, CA • On-site, Remote

$112K - $142K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 16 days ago

Job description

ML Research Scientist, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry.
At Genesis Molecular AI, we're a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
We don't just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative and discriminative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.
About the Role
This is an opportunity for a scientific innovator to advance the future of generative AI in drug discovery. As a key member of the Genesis AI team, you will shape and drive our research agenda for foundation models. You will lead critical research initiatives in areas like reinforcement learning, novel model architectures, and advanced pretraining and post-training methods. Your core mission is to create groundbreaking models and insights that are instrumental in discovering new medicines.
This role requires a deep curiosity and a collaborative spirit. You will be a strong team player, working in close partnership with our exceptional engineers and drug discovery experts to bring complex ideas to life. We also want you to be a voice in the scientific community. We will actively support and champion your efforts to publish some research breakthroughs in premier ML venues such as NeurIPS, ICML, and ICLR.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You will
  • Lead transformative research projects from conception to implementation, tackling core challenges in generative modeling for molecular systems.
  • Design and develop novel models and algorithms, building upon the latest literature in diffusion models, flow matching, RL, LLMs, and other cutting-edge areas.
  • Execute ambitious experiments at scale, leveraging our world-class computational infrastructure to rigorously validate your hypotheses and push the boundaries of the state-of-the-art.
  • Collaborate intensely with a multidisciplinary team to translate your research into tangible impact on our drug discovery platform.
  • Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
  • Mentor and guide other researchers and engineers, fostering a culture of shared learning, growth, and innovation.

You are
  • A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
  • An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
  • A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
  • A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary - only willingness to learn.
  • A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
  • Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.

Nice to have's
  • PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
  • Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
  • Experience in distributed training and inference of large models on GPU clusters.
  • Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.

Compensation, Benefits, and Perks
  • Competitive compensation package that includes salary and equity.
  • Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
  • 401(k) plan.
  • Open (unlimited) PTO policy.
  • Free lunches and dinners at our offices.
  • Paid family leave (maternity and paternity).
  • Life and long- and short-term disability insurance.

About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.

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