ZipRecruiter

Log In

1

Physics Based Machine Learning Jobs in El Segundo, CA

Applied Physics

Seismologist

Los Angeles, CA · On-site

Organize, analyze, and interpret large geophysical sets from ongoing laboratory/field operations and physics-based simulations * Employ physical, statistical, or machine learning-based methods for ...

Applied Physics

Seismologist

Los Angeles, CA · On-site

Organize, analyze, and interpret large geophysical sets from ongoing laboratory/field operations and physics-based simulations * Employ physical, statistical, or machine learning-based methods for ...

Applied Physics

Seismologist

Los Angeles, CA · On-site

Organize, analyze, and interpret large geophysical sets from ongoing laboratory/field operations and physics-based simulations * Employ physical, statistical, or machine learning-based methods for ...

Motorola

Machine Learning Engineer

Los Angeles, CA

$150K - $180K/yr

Advanced degree in a quantitative field such as electrical and computer engineering, physics ... Familiarity with cloud-based infrastructure: Azure and/or AWS * Experience tracking projects with ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

... Physics, or another quantitative field. * 5+ years building, evaluating, and deploying machine ... Actual salaries will vary and may be above or below the range based on various factors including ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

... Physics, or another quantitative field. * 5+ years building, evaluating, and deploying machine ... Actual salaries will vary and may be above or below the range based on various factors including ...

Machina Labs

Machine Learning Engineer

Los Angeles, CA

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ... Duties and responsibilities may change based on business needs. The base salary range for this role ...

Hadrian Automation

Machine Learning Engineer

Torrance, CA · On-site

$160K - $250K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling ... An employee's pay position within the salary range will be based on several factors, including, but ...

next page

Showing results 1-20

All JobsPhysics Based Machine Learning JobsPhysics Based Machine Learning Jobs in El Segundo, CA

Physics Based Machine Learning information

See El Segundo, CA salary details

$5

$21

$27

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

As of Aug 13, 2026, the average hourly pay for physics based machine learning in El Segundo, CA is $21.37, according to ZipRecruiter salary data. Most workers in this role earn between $13.32 and $27.16 per hour, depending on experience, location, and employer.

What does a physics based machine learning professional do?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What is a physics based machine learning?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What are the key skills and qualifications needed to thrive in physics based machine learning?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What are popular job titles related to Physics Based Machine Learning jobs in El Segundo, CA? For Physics Based Machine Learning jobs in El Segundo, CA, the most frequently searched job titles are:
What cities near El Segundo, CA are hiring for Physics Based Machine Learning jobs? Cities near El Segundo, CA with the most Physics Based Machine Learning job openings:
Physics Based Machine Learning jobs near you

Principal Machine Learning Researcher (Physical AI)

Freeform

Los Angeles, CA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted yesterday

Job description

PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI)
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. By treating manufacturing as a single integrated system, we unlock a new era of innovation where complex hardware is designed, built, and scaled without limits.
This architecture enables continuous generation of petabyte-scale, high-fidelity data capturing the physics of metal printing - from in-situ process signals and machine state to geometry and material outcomes. Each factory node contributes to a growing learning system that improves modeling accuracy, control performance, yield, and scalability over time.
Freeform is hiring a Principal Machine Learning Researcher to lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. The role focuses on developing machine learning methods that integrate large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks. Work includes modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing, using hybrid physics-ML approaches and multi-modal in-situ data.
Research is validated against physical outcomes and deployed into production systems, where improvements directly impact stability, yield, throughput, and capability across an expanding fleet of manufacturing nodes. Your work will have a direct and meaningful impact on how frontier technologies are designed and produced at scale.
Responsibilities:
  • Design and develop machine learning models for complex, multi-physics manufacturing processes.
  • Develop hybrid modeling approaches that combine first-principles physics with data-driven learning.
  • Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems.
  • Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing.
  • Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.
  • Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes.
  • Integrate learned models with physics-based simulation and digital twin frameworks.
  • Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware.
  • Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics.
  • Guide the integration of machine learning models into production software and manufacturing workflows.
  • Help define research direction and technical standards for machine learning applied to physical systems within the organization.

Basic Qualifications:
  • 5+ years of experience in machine learning, applied research, or related technical fields or a PhD in machine learning, applied mathematics, physics, robotics, controls, or a closely related discipline.
  • Strong foundations in machine learning applied to physical systems, modeling, or control.
  • Proficiency in Python and at least one systems-level programming language (C/C++ preferred).
  • Experience working with large-scale, noisy, real-world datasets.

Nice to Have:
  • MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline.
  • Experience with hybrid physics-ML models, digital twins, or simulation-in-the-loop learning.
  • Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems.
  • Experience with image-based or sensor-based inference in industrial or scientific settings.
  • Familiarity with computational geometry or geometric modeling.
  • Comfort working across theory, experimentation, and deployment in tightly coupled systems.
  • Ability to reason from first principles and translate theory into working models and systems.

Location:
  • Based in Hawthorne, our vertically integrated facility brings technology development, R&D, and production together under one roof. We operate at the center of LA's deep tech ecosystem, surrounded by some of the most ambitious hardware innovation happening anywhere in the country.
  • Our fast-paced, cross-functional environment is built on close collaboration, and as such, this role requires full-time onsite presence (five days a week), with very limited exceptions.

What We Offer:
  • We have an inclusive and diverse culture that values collaboration, learning, and making deliberate data-driven decisions.
  • We offer a unique opportunity to be an early and integral member of a rapidly growing company that is scaling a world-changing technology.
  • Benefits
    • Significant stock option packages
    • 100% employer-paid Medical, Dental, and Vision insurance (premium PPO and HMO options)
    • Life insurance
    • Traditional and Roth 401(k)
    • Relocation assistance provided
    • Paid vacation, sick leave, and company holidays
    • Generous Paid Parental Leave and extended transition back to work for the birthing parent
    • Free daily catered lunch and dinner, and fully stocked kitchenette
    • Casual dress, flexible work hours, and regular catered team building events
  • Compensation
    • As a growing company, the salary range is intentionally wide as we determine the most appropriate package for each individual taking into consideration years of experience, educational background, and unique skills and abilities as demonstrated throughout the interview process. Our intent is to offer a salary that is commensurate for the company's current stage of development and allows the employee to grow and develop within a role.
    • In addition to the significant stock option package, the estimated salary range for this role is $200,000-$400,000. However is this a unique position with outsized impact for the right game-changing hire, so we will consider compensation outside of this range on a case-by-case basis.
  • Freeform is an Equal Opportunity Employer that values diversity; employment with Freeform is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

Apply