They are seeking a Physics Informed Machine Learning Scientist to join a research team focused on creating next-generation lithography light source technologies through advanced data management and ...
They are seeking a Physics Informed Machine Learning Scientist to join a research team focused on creating next-generation lithography light source technologies through advanced data management and ...
The Physics Informed Machine Learning Scientist works on the Virtual Source team building integrated master-model frameworks to capture the tightly coupled, multi-physics behavior of the system ...
The Physics Informed Machine Learning Scientist works on the Virtual Source team building integrated master-model frameworks to capture the tightly coupled, multi-physics behavior of the system ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies ...
Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies with ...
Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies with ...
Physics-Informed Machine Learning Specialist
Livermore, CA · On-site
$9.7K/wk
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies ...
Physics-Informed Machine Learning Specialist
Livermore, CA · On-site
$9.7K/wk
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies ...
Machine Learning Team Lead
San Francisco, CA · On-site
$250K - $295K/yr
Advancing physics-informed, AI-driven solvers and surrogate architectures * Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities * Driving R&D programs through ...
Machine Learning Team Lead
San Francisco, CA · On-site
$250K - $295K/yr
Advancing physics-informed, AI-driven solvers and surrogate architectures * Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities * Driving R&D programs through ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Quick apply
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization ...
Machine Learning Engineer
$150K - $277K/yr
Build differentiable simulation and physics-informed machine learning pipelines to analyze and improve cameras and sensors. Ground the exploration via validated simulation and metrology results to ...
Machine Learning Engineer
$150K - $277K/yr
Build differentiable simulation and physics-informed machine learning pipelines to analyze and improve cameras and sensors. Ground the exploration via validated simulation and metrology results to ...
POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...
POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...
Postdoctoral Scholar
University Park, PA · On-site
POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...
Postdoctoral Scholar
University Park, PA · On-site
POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...
Machine Learning Engineer
Grovetown, GA · On-site
Exposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models) * Background in CFD, simulation, computational mechanics, or applied ...
Machine Learning Engineer
Grovetown, GA · On-site
Exposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models) * Background in CFD, simulation, computational mechanics, or applied ...
Exposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models) * Background in CFD, simulation, computational mechanics, or applied ...
Exposure to scientific / physics-informed machine learning (surrogate modeling, embedding physical constraints into ML models) * Background in CFD, simulation, computational mechanics, or applied ...
NIST PREP Postdoc Associate in Process Modeling using Physically Informed Machine Learning
Gaithersburg, MD · On-site
$84K - $92K/yr
Designing and training physics-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics ...
NIST PREP Postdoc Associate in Process Modeling using Physically Informed Machine Learning
Gaithersburg, MD · On-site
$84K - $92K/yr
Designing and training physics-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics ...
Data Scientist
Houston, TX · On-site
This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms. You will develop and productionize advanced ...
New
Data Scientist
Houston, TX · On-site
This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms. You will develop and productionize advanced ...
New
Research Scientist, AI
San Francisco, CA · On-site
$150K - $275K/yr
Implement surrogate models, physics-informed neural networks, or generative approaches for scientific problems * Develop data pipelines and frameworks for scientific machine learning across ...
Research Scientist, AI
San Francisco, CA · On-site
$150K - $275K/yr
Implement surrogate models, physics-informed neural networks, or generative approaches for scientific problems * Develop data pipelines and frameworks for scientific machine learning across ...
... machine learning models, applying physics-informed machine learning to real-world engineering systems, deploying scalable production tools on HPC infrastructure, and translating computational ...
... machine learning models, applying physics-informed machine learning to real-world engineering systems, deploying scalable production tools on HPC infrastructure, and translating computational ...
Build and train surrogate, operator-learning, or physics-informed models against experimental and ... Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics ...
Build and train surrogate, operator-learning, or physics-informed models against experimental and ... Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics ...
Physics-Informed Machine Learning (PIML): Embed physical constraints (conservation laws, symmetries, and PDEs) directly into the loss functions and inductive biases of deep learning models to ensure ...
Physics-Informed Machine Learning (PIML): Embed physical constraints (conservation laws, symmetries, and PDEs) directly into the loss functions and inductive biases of deep learning models to ensure ...
Physics Informed Machine Learning information
See salary details
$5.29 - $7.12
0% of jobs
$7.12 - $8.96
0% of jobs
$8.96 - $10.80
0% of jobs
$10.80 - $12.63
24% of jobs
$12.72 is the 25th percentile. Wages below this are outliers.
$12.63 - $14.47
16% of jobs
$14.47 - $16.30
0% of jobs
$16.30 - $18.14
0% of jobs
$18.14 - $19.97
0% of jobs
$19.97 - $21.81
0% of jobs
The median wage is $22.25 / hr.
$21.81 - $23.65
40% of jobs
$23.65 - $25.48
19% of jobs
$5
$20
$25
How much do physics informed machine learning jobs pay per hour?
As of Jul 11, 2026, the average hourly pay for physics informed machine learning in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 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.
More about Physics Informed Machine Learning jobs
What cities are hiring for Physics Informed Machine Learning jobs? Cities with the most Physics Informed Machine Learning job openings:
What states have the most Physics Informed Machine Learning jobs? States with the most job openings for Physics Informed Machine Learning jobs include:
What job categories do people searching Physics Informed Machine Learning jobs look for? The top searched job categories for Physics Informed Machine Learning jobs are:
- Machine Learning Ops Engineer
- International Machine Learning Engineer
- Tesla Machine Learning Engineer
- Principal Machine Learning Engineer
- Machine Learning Algorithms
- Google Machine Learning Engineer
- Internship Deep Reinforcement Learning
- Founding Machine Learning Engineer
- Physics Based Machine Learning
- Machine Learning Engineer Opt
ASML rating
9.3
Based on 41 frontline employees who took The Breakroom Quiz
9th of 429 rated machine equipment manufacturers
Job description
Job Summary:
ASML is a leading company in developing lithography machines for the microchip industry. They are seeking a Physics Informed Machine Learning Scientist to join a research team focused on creating next-generation lithography light source technologies through advanced data management and machine learning methodologies.
Responsibilities:
• Establish a scalable data management framework spanning legacy and new datasets from test benches and source prototypes, ensuring data quality, accessibility, and structured readiness for seamless integration into ML workflows.
• Develop physics-informed machine learning models and scientific simulations to enable system-level tradeoff analysis and drive the definition and optimization of lithography source technology configurations.
• Adapt and integrate existing physics-based models into a master virtual model, and establish the necessary infrastructure for deployment and maintenance.
• Propose experimental anchoring studies, analyze test results, reduce model uncertainty through correlation building, and extract actionable knowledge from submodule- to full-system-level analysis.
• Provide input to technology roadmaps, identify de-risking activities and key scientific learning objectives, and contribute to experimental design to establish design guidelines, performance requirements, and procedures for product teams.
• Troubleshoot code and algorithms required for source operation, data streaming, storage, and queries.
• Document learnings and communicate knowledge to engineering and product development teams to guide product improvement and the release of new product nodes.
• Work independently and collaboratively to deliver on stated objectives, whether pursuing new knowledge, demonstrating new capabilities, or characterizing existing performance.
• Perform other duties as assigned or required.
Qualifications:
Required:
• Ph.D. with a minimum of 3+ years of experience or a Master’s degree with at least 6+ years of experience in an analytical field such as mathematics, physics, or engineering, with extensive experience in physics-informed machine learning and model integration into scalable master models.
• Experience solving complex, open-ended modeling problems using optimization and deep learning methodologies, with strong expertise in data management and building scalable data and training pipelines for end-to-end model development and training.
• Strong software development skills in Python, with experience in deep learning frameworks (e.g. PyTorch or JAX); proficiency in C/C++, and Matlab is a plus.
• Ability to clearly and logically communicate ideas and knowledge to various audiences.
• Demonstrated ability to work effectively as a part of a team and lead investigation and research efforts involving multiple stakeholders and constraints.
• Proven ability to build trust and credibility, enabling effective leadership through influence.
• The successful candidate will not only have excelled in their technical field, but will have demonstrated inter-personal and communications strengths.
• Deep understanding of scientific research methods and strong curiosity.
Preferred:
• Experience with database tools, automation frameworks, and experimental tracking platforms (e.g. MLflow) for managing end to end ML lifecycle.
• Experience working in cloud and development environments such as Azure Kubernetes Service (AKS), Google Distributed Cloud Edge (GDCE), Apache Spark, Azure Databricks, and related technologies.
Company:
ASML is a manufacturer of chip-making equipment. Founded in 1984, the company is headquartered in Veldhoven, NLD, with a team of 10001+ employees. The company is currently Late Stage.
ASML is a leading company in developing lithography machines for the microchip industry. They are seeking a Physics Informed Machine Learning Scientist to join a research team focused on creating next-generation lithography light source technologies through advanced data management and machine learning methodologies.
Responsibilities:
• Establish a scalable data management framework spanning legacy and new datasets from test benches and source prototypes, ensuring data quality, accessibility, and structured readiness for seamless integration into ML workflows.
• Develop physics-informed machine learning models and scientific simulations to enable system-level tradeoff analysis and drive the definition and optimization of lithography source technology configurations.
• Adapt and integrate existing physics-based models into a master virtual model, and establish the necessary infrastructure for deployment and maintenance.
• Propose experimental anchoring studies, analyze test results, reduce model uncertainty through correlation building, and extract actionable knowledge from submodule- to full-system-level analysis.
• Provide input to technology roadmaps, identify de-risking activities and key scientific learning objectives, and contribute to experimental design to establish design guidelines, performance requirements, and procedures for product teams.
• Troubleshoot code and algorithms required for source operation, data streaming, storage, and queries.
• Document learnings and communicate knowledge to engineering and product development teams to guide product improvement and the release of new product nodes.
• Work independently and collaboratively to deliver on stated objectives, whether pursuing new knowledge, demonstrating new capabilities, or characterizing existing performance.
• Perform other duties as assigned or required.
Qualifications:
Required:
• Ph.D. with a minimum of 3+ years of experience or a Master’s degree with at least 6+ years of experience in an analytical field such as mathematics, physics, or engineering, with extensive experience in physics-informed machine learning and model integration into scalable master models.
• Experience solving complex, open-ended modeling problems using optimization and deep learning methodologies, with strong expertise in data management and building scalable data and training pipelines for end-to-end model development and training.
• Strong software development skills in Python, with experience in deep learning frameworks (e.g. PyTorch or JAX); proficiency in C/C++, and Matlab is a plus.
• Ability to clearly and logically communicate ideas and knowledge to various audiences.
• Demonstrated ability to work effectively as a part of a team and lead investigation and research efforts involving multiple stakeholders and constraints.
• Proven ability to build trust and credibility, enabling effective leadership through influence.
• The successful candidate will not only have excelled in their technical field, but will have demonstrated inter-personal and communications strengths.
• Deep understanding of scientific research methods and strong curiosity.
Preferred:
• Experience with database tools, automation frameworks, and experimental tracking platforms (e.g. MLflow) for managing end to end ML lifecycle.
• Experience working in cloud and development environments such as Azure Kubernetes Service (AKS), Google Distributed Cloud Edge (GDCE), Apache Spark, Azure Databricks, and related technologies.
Company:
ASML is a manufacturer of chip-making equipment. Founded in 1984, the company is headquartered in Veldhoven, NLD, with a team of 10001+ employees. The company is currently Late Stage.
About ASML
Sourced by ZipRecruiter
Industry
Manufacturing
Company size
10,000+ Employees
Headquarters location
Chandler, AZ, US
Year founded
1973