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Physics Informed Machine Learning Jobs in Pasadena, CA

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA · On-site

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

SpaceX

Software Engineer, Simulation

Hawthorne, CA

$145K - $175K/yr

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural networks-to accelerate simulations, enhance efficiency, drive novel improvements, increase part yield ...

SpaceX

Sr. Software Engineer, Computer Vision

Hawthorne, CA · On-site

$124K - $163K/yr

OR 8+ years of professional experience building and deploying AI software/Machine Learning in lieu ... physics-informed neural networks (PINNs) or surrogate modeling Company : SpaceX develops and ...

Applied Physics

Seismologist

Los Angeles, CA

Experience with earthquake physics, modeling geophysical behavior, and the application of machine learning methods to earth science problems * Experience with geophysical monitoring or analysis of ...

Applied Physics

Seismologist

Los Angeles, CA · On-site

Experience with earthquake physics, modeling geophysical behavior, and the application of machine learning methods to earth science problems * Experience with geophysical monitoring or analysis of ...

Applied Physics

Seismologist

Los Angeles, CA · On-site

Experience with earthquake physics, modeling geophysical behavior, and the application of machine learning methods to earth science problems * Experience with geophysical monitoring or analysis of ...

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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 Jun 16, 2026, the average hourly pay for physics informed machine learning in Pasadena, CA is $21.88, according to ZipRecruiter salary data. Most workers in this role earn between $13.65 and $27.79 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 Pasadena, CA? For Physics Informed Machine Learning jobs in Pasadena, CA, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Pasadena, CA look for? The top searched job categories for Physics Informed Machine Learning jobs in Pasadena, CA are:
What cities near Pasadena, CA are hiring for Physics Informed Machine Learning jobs? Cities near Pasadena, 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 Pasadena, CA as of June 2026, with employment types broken down into 1% Locum Tenens, 80% Full Time, 15% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $45,520 per year, or $21.9 per hour.
ML Engineer, Surrogate Modeling (Vehicle Engineering)

ML Engineer, Surrogate Modeling (Vehicle Engineering)

SpaceX

Hawthorne, CA • On-site

Full-time

Posted 25 days ago

SpaceX rating

8.7

Company rating: 8.7 out of 10

Based on 144 frontline employees who took The Breakroom Quiz

13th of 60 rated aerospace companies

Job description

Job Summary:
SpaceX is a company focused on developing technologies for space exploration. The ML Engineer will be part of the AI for Vehicle Engineering team, developing high-performance surrogate models to solve complex engineering problems for launch vehicles and spacecraft.
Responsibilities:
• Develop, train, evaluate, and deploy production-grade AI surrogate models that accelerate critical engineering simulation workflows
• Design and implement State-of-the-Art (SOTA) neural architectures and training strategies tailored to complex engineering problem domains
• Build scalable data pipelines to preprocess, manage, and utilize tens of thousands of high-fidelity simulation results
• Stay current with the latest research in neural operators, physics-informed ML, and surrogate modeling, implementing new techniques when needed
• Collaborate with peers on architecture, design, and code reviews
• Deep dive into engineering problems to identify where AI can deliver the highest leverage and most reliable solutions
• Develop and apply techniques for uncertainty quantification, active learning, and inverse problems (e.g., geometry and shape optimization)
• Ensure all AI systems are rigorously validated and vetted for accuracy, robustness, and reliability in engineering use
Qualifications:
Required:
• Bachelor’s degree in computer science, data science, engineering, math, physics, or a related technical discipline; OR 4+ years of professional experience building software in lieu of a degree
• 1+ years of software development experience in Python for machine learning, AI, or data science applications
• Ability to work extended hours and weekends as necessary
Preferred:
• Master’s or PhD in computer science, machine learning, engineering, or a related field with a focus on surrogate modeling or AI for scientific/engineering simulation
• Demonstrated experience training, tuning, and deploying production-grade ML surrogate models in real engineering workflows
• Expert-level understanding of at least one modern architecture class such as Fourier Neural Operators (FNO), neural operators, MeshGraphNet, Transolver, graph neural networks, physics-informed neural networks, or other surrogate model architecture
• Experience solving inverse problems such as geometry optimization or design under uncertainty
• Strong understanding of traditional simulation and numerical methods (CFD, FEA, thermal analysis, etc) and how to integrate them with surrogate models
• Experience with uncertainty quantification techniques for surrogate models
• Hands-on experience building active learning or adaptive sampling pipelines
• Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Experience with surrogate modeling libraries such as NVIDIA PhysicsNemo or similar
• Experience developing on Linux systems with GPU accelerators
• Strong understanding of software engineering best practices including version control, testing, and continuous integration
• Solid foundation in statistics, numerical methods, and core machine learning algorithms
Company:
SpaceX develops and operates rockets, satellite networks, and AI infrastructure including launch, connectivity, and cloud services. Founded in 2002, the company is headquartered in Hawthorne, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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