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Physics Informed Machine Learning Jobs in Berkeley, CA

nudge

Lead Machine Learning Engineer

San Francisco, CA · On-site

$120K - $159K/yr

About the role As a Machine Learning Lead at Nudge, you will drive the development of next ... Strong first-principles understanding of engineering, physics, and signal processing. * Experience ...

Nudge

Lead Machine Learning Engineer

San Francisco, CA · On-site

$120K - $159K/yr

About the role As a Machine Learning Lead at Nudge, you will drive the development of next ... Strong first-principles understanding of engineering, physics, and signal processing. * Experience ...

Adobe

Senior Machine Learning Engineer

San Francisco, CA · On-site

$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 ...

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

As of Jul 5, 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:
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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

Machine Learning: Multimodal Foundation Models

The Bot Company

San Francisco, CA • On-site

$200K - $350K/yr

Full-time

Posted 9 days ago

Job description

The Bot Company
We're building a helpful robot for every home.
We're a small team of engineers, designers, and operators based in San Francisco. Our team comes from Tesla, Cruise, OpenAI, Google, Pixar, and many other great companies. In the past we've shipped to hundreds of millions of users and know what it takes to build amazing products and experiences.
Our team is deliberately lean to promote rapid decision making and do away with bureaucracy and hierarchy. Everyone is an IC and is empowered with massive scope, radical ownership, and direct responsibility. We work across the stack with a culture built for rapid iteration and fast execution.
What we look for in all candidates
All roles at The Bot Company demand extreme sharpness and the ability to move fast in high-intensity environments. Throughout the process, we expect candidates to demonstrate:
  • Exceptional mental acuity: you think quickly, learn instantly, and reason across unfamiliar domains.
  • Engineering curiosity: you naturally dig into how systems work, even outside your specialty.
  • High performance mindset: you move fast, handle ambiguity, and excel when the environment is demanding.
Machine Learning: Multimodal Foundation Models
We are building unified foundation models that natively reason across text, image, video, and kinematics to drive intelligent robotic policies.
You will work on large multi-modal networks and own the entire stack from data to training and deploying models.
What You'll Do
  • Build Native Multimodal Policies: Develop architectures where vision, language, and more modalities share a unified representation.
  • Improve Cross-Modal Reasoning: Research and implement methods to ensure the model doesn't just "associate" modalities but actually reasons through them (e.g., grounding visual physics in kinematic constraints).
  • Own the Training Loop End-to-End: Design, run, debug, and iterate on large-scale training experiments; diagnosing failure modes, improving data mixtures, and tightening evaluation to drive measurable gains.
  • Ship and Iterate on Real Systems: Integrate models into real robotic stacks, build on robot code to deploy your models, and optimize performance for edge inference.
Requirements
  • Very strong coding skills in Python, C++, or Rust.
  • Production MLLM Experience: Track record of training and deploying large-scale multimodal models.
  • Pretraining & RL Mastery: Deep intuition for LLM-style pretraining, post-training, and Reinforcement Learning at scale.
  • Infrastructure Fluency: Comfortable managing and optimizing large-scale experiments on massive GPU clusters.

Why Join
You'll work with a small, elite team on challenges that require speed, intelligence, and deep engineering instinct. If you enjoy understanding systems at all levels, move fast, and think even faster, you'll thrive here.

Apply