ZipRecruiter

Log In

1

Physics Informed Machine Learning Jobs in Santa Barbara, CA

Toyon

Deep Learning Algorithm Developer

Goleta, CA

$120K - $200K/yr

... AI) / Machine Learning (ML) techniques. Experience in Computer Vision is desired for current ... physics, and/or mathematics. Experience with PyTorch, TensorFlow, or other deep learning frameworks ...

Toyon

AI/ML Software Engineer

Goleta, CA

$120K - $200K/yr

... Machine Learning (ML) applications. Our software engineers work closely with our researchers and ... Candidates should possess strong math and physics knowledge to enable developing advanced AI/ML ...

Toyon

Algorithm Developer

Goleta, CA · On-site

$120K - $200K/yr

... machine learning, estimation theory, computer vision, computational imaging, and/or electromagnetics. Requirements Candidates should have a strong background in engineering, computer science, physics ...

Toyon Research Corporation

Algorithm Developer

Goleta, CA · On-site

$120K - $200K/yr

... machine learning, estimation theory, computer vision, computational imaging, and/or electromagnetics. Requirements Candidates should have a strong background in engineering, computer science, physics ...

Umbra

Senior Radar Systems Engineer

Santa Barbara, CA · On-site

$116K - $159K/yr

S. space leadership while keeping the world safe and informed. About the Team Remote Sensing - The ... Proven experience in machine learning. * 10+ years experience with Python's scientific stack: Numpy ...

Umbra

Senior Radar Systems Engineer

Santa Barbara, CA · On-site

$116K - $159K/yr

S. space leadership while keeping the world safe and informed. About the Team Remote Sensing - The ... Proven experience in machine learning. * 10+ years experience with Python's scientific stack: Numpy ...

next page

Showing results 1-20

All JobsPhysics Informed Machine Learning JobsPhysics Informed Machine Learning Jobs in Santa Barbara, CA

Physics Informed Machine Learning information

See Santa Barbara, CA salary details

$5

$22

$28

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

As of Jul 27, 2026, the average hourly pay for physics informed machine learning in Santa Barbara, CA is $22.32, according to ZipRecruiter salary data. Most workers in this role earn between $13.89 and $28.37 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 Santa Barbara, CA? For Physics Informed Machine Learning jobs in Santa Barbara, CA, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Santa Barbara, CA look for? The top searched job categories for Physics Informed Machine Learning jobs in Santa Barbara, CA are:
What cities near Santa Barbara, CA are hiring for Physics Informed Machine Learning jobs? Cities near Santa Barbara, 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 Barbara, CA as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $46,433 per year, or $22.3 per hour.

Machine Learning Engineer

Quantum Machines

Santa Barbara, CA • On-site

Full-time

Posted 28 days ago

Job description

Description
Quantum Machines (QM) is a global leader in quantum computing control systems. Through our pioneering hardware and software solutions based on instruction-based quantum control, we're revolutionizing how quantum computers are built and controlled. As we stand at the forefront of exponential growth in quantum computing, we're assembling an elite team that actively shapes the evolution of quantum technologies.
We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.
You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.
Responsibilities:
  • Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
  • Develop real-time parameter steering for calibration during QEC and between circuits.
  • Develop and maintain agentic frameworks for autonomous system control and calibration.
  • Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
  • Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
  • Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

Requirements
  • PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. 4+ years of relevant experience
  • Strong background in Machine Learning and Deep Learning, with hands-on experience in at least one of: deep learning, reinforcement learning, agentic AI
  • Strong Python proficiency, including scientific or systems-oriented codebases
  • Solid software engineering fundamentals (architecture, Git workflows, testing, code review)
  • Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer well
  • Strong problem-solving skills and customer-focused mindset; ability to work independently and in multidisciplinary teams
  • Proven software development track record and excellent technical communication skills
  • Familiarity with quantum computing concepts - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
  • Experience with sim-to-real, multi-objective RL, or meta-learning- advantage

Apply