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Physics Informed Machine Learning Jobs in Berkeley, CA

MSCI

Machine Learning Researcher

San Francisco, CA · On-site

$144K - $187K/yr

Conduct research using machine learning methodologies that integrate financial theory with deep ... PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ...

MSCI

Machine Learning Researcher

San Francisco, CA

$144K - $187K/yr

MSCI is establishing a Machine Learning Center of Excellence within the Research & Development team ... PhD in Computer Science, Statistics, Mathematics, Physics, Operations Research, or related ...

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 ...

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All JobsPhysics Informed Machine Learning JobsPhysics Informed Machine Learning Jobs in Berkeley, CA

Physics Informed Machine Learning information

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$6

$24

$31

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

As of Jun 14, 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:
What job categories do people searching Physics Informed Machine Learning jobs in Berkeley, CA look for? The top searched job categories for Physics Informed Machine Learning jobs in Berkeley, CA are:
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
Infographic showing various Physics Informed Machine Learning job openings in Berkeley, CA as of June 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 16% Part Time, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $51,097 per year, or $24.6 per hour.

Machine Learning Operations (MLOps)

The Hiring Method LLC

Fremont, CA

$76K - $102K/yr

Other

Posted 2 days ago

Job description

Machine Learning Operations (MLOps) Engineer

A global leader in photonics and semiconductor technology is seeking a Machine Learning Operations (MLOps) Engineer to help develop, deploy, and scale AI/ML solutions within advanced manufacturing operations.

This is a highly visible, cross-functional role focused on applying machine learning and artificial intelligence to improve manufacturing yield, process control, defect detection, and operational efficiency. The successful candidate will work directly with Process Engineering, Product Engineering, Test Engineering, Manufacturing, MES, and IT teams to build data pipelines, develop machine learning models, and deploy production-ready AI solutions into manufacturing workflows.

This role offers a rare opportunity to pioneer AI/ML capabilities within a cutting-edge semiconductor and photonics manufacturing environment while directly impacting yield improvement and cost reduction initiatives.

What You'll Do

  • Partner with Process, Product, and Test Engineering teams to understand manufacturing workflows, data sources, and business objectives
  • Develop, train, validate, and optimize machine learning models for manufacturing applications
  • Build and maintain reliable data pipelines supporting model development and deployment
  • Apply supervised and unsupervised learning techniques to improve process control, yield, and defect detection
  • Define, monitor, and report KPIs related to model performance and manufacturing outcomes
  • Deploy machine learning models into production environments using APIs, containers, and orchestration platforms
  • Integrate AI/ML solutions with existing manufacturing systems, databases, MES platforms, and on-premise infrastructure
  • Collaborate with Operations and Engineering stakeholders to identify new AI/ML opportunities
  • Monitor model performance, retrain models as necessary, and drive continuous improvement initiatives
  • Document methodologies, validation approaches, performance results, and improvement plans
  • Support knowledge transfer and collaboration with partner manufacturing sites deploying similar AI/ML solutions

What You Bring

  • Bachelor's degree in Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, Data Science, Machine Learning, or related field required
  • 5+ years of relevant experience, or Master's degree with 2+ years of experience
  • Strong expertise with at least one deep learning framework such as PyTorch, TensorFlow, or Keras
  • Experience with deep learning architectures such as CNNs, RNNs, VAEs, GANs, or related models
  • Experience with tree-based learning methods including Random Forests, Gradient Boosting, or similar approaches
  • Strong understanding of data preprocessing techniques including normalization, denoising, feature engineering, and missing data handling
  • Experience with model development best practices including hyperparameter tuning, overfitting prevention, model validation, and k-fold cross-validation
  • Experience deploying machine learning models using REST APIs, containerization, and orchestration technologies
  • Strong Python programming and data analysis skills
  • Ability to work effectively across engineering, manufacturing, and operations teams
  • Proven track record of developing and deploying production-ready AI/ML solutions

Preferred Qualifications

  • Experience with CUDA, ONNX, LibTorch, C++, and high-performance inference environments
  • Experience with machine vision, computer vision, OCR, defect detection, or image analytics
  • Knowledge of clustering, dimensionality reduction, and feature extraction techniques
  • Familiarity with AWS, Azure, GCP, or cloud-based AI/ML environments
  • Semiconductor manufacturing experience
  • Experience supporting manufacturing, quality, yield improvement, or industrial AI applications
  • Experience working with large manufacturing datasets and operational analytics

What You Get

  • Opportunity to build one of the first dedicated AI/ML programs within a major semiconductor manufacturing operation
  • Direct impact on yield improvement, manufacturing efficiency, and product quality
  • Exposure to cutting-edge photonics and optical networking technologies supporting AI infrastructure growth
  • Highly visible role with significant cross-functional collaboration
  • Opportunity to influence manufacturing operations on a global scale
  • Strong technical autonomy and ownership
  • Potential pathway into a long-term AI/ML leadership role based on performance and business growth
  • Collaborative environment with experienced engineering, manufacturing, and product development teams
  • Opportunity to apply advanced machine learning techniques to real-world industrial challenges

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