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 ...
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 ...
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 ...
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 ...
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 ...
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 Engineer
Sunnyvale, CA · On-site
$147K - $272K/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
Sunnyvale, CA · On-site
$147K - $272K/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 ...
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 ...
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 (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 ...
PhD or MS in Computer Science, Machine Learning, or related technical field * Familiarity with ... Background in physics-informed neural networks or scientific ML * Experience with high-performance ...
PhD or MS in Computer Science, Machine Learning, or related technical field * Familiarity with ... Background in physics-informed neural networks or scientific ML * Experience with high-performance ...
Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g ... Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs) * Experience training models ...
Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g ... Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs) * Experience training models ...
... with physics-based machine learning - including physics-informed neural networks, simulation-to-real transfer, or learned physical models Cross-disciplinary collaboration experience - hardware ...
... with physics-based machine learning - including physics-informed neural networks, simulation-to-real transfer, or learned physical models Cross-disciplinary collaboration experience - hardware ...
... physics-informed ML, and surrogate modeling, implementing new techniques when needed • ... for machine learning, AI, or data science applications • Ability to work extended hours and ...
... physics-informed ML, and surrogate modeling, implementing new techniques when needed • ... for machine learning, AI, or data science applications • Ability to work extended hours and ...
... machine learning - including ... physics-informed neural networks, simulation-to-real transfer, or learned physical modelsCross ...
... machine learning - including ... physics-informed neural networks, simulation-to-real transfer, or learned physical modelsCross ...
Machine Learning
Mountain View, CA · On-site
... Physics, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying machine learning techniques and driving product direction). Company
Machine Learning
Mountain View, CA · On-site
... Physics, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying machine learning techniques and driving product direction). Company
Experience with physics-based machine learning -- including physics-informed neural networks, simulation-to-real transfer, or learned physical models * Cross-disciplinary collaboration experience ...
Experience with physics-based machine learning -- including physics-informed neural networks, simulation-to-real transfer, or learned physical models * Cross-disciplinary collaboration experience ...
Senior Quantum Applied Research Scientist, Calibration and Decoding
Santa Clara, CA · Hybrid
$115K - $147K/yr
Hands-on expertise in machine learning and deep learning for science or physics, including model ... Experience with physics-informed or generative approaches to synthetic data generation, including ...
Senior Quantum Applied Research Scientist, Calibration and Decoding
Santa Clara, CA · Hybrid
$115K - $147K/yr
Hands-on expertise in machine learning and deep learning for science or physics, including model ... Experience with physics-informed or generative approaches to synthetic data generation, including ...
Senior Machine Learning Engineer - Engineering Intelligence Systems
Calabasas, CA · On-site
$110K - $151K/yr
... physics-informed features, and feedback signals refine model accuracy and generalization across ... Responsibilities Role Overview This role sits at the intersection of machine learning, data ...
Senior Machine Learning Engineer - Engineering Intelligence Systems
Calabasas, CA · On-site
$110K - $151K/yr
... physics-informed features, and feedback signals refine model accuracy and generalization across ... Responsibilities Role Overview This role sits at the intersection of machine learning, data ...
Senior Machine Learning Engineer - Engineering Intelligence Systems
Calabasas, CA · On-site
$110K - $151K/yr
... physics-informed features, and feedback signals refine model accuracy and generalization across ... Responsibilities Role Overview This role sits at the intersection of machine learning, data ...
Senior Machine Learning Engineer - Engineering Intelligence Systems
Calabasas, CA · On-site
$110K - $151K/yr
... physics-informed features, and feedback signals refine model accuracy and generalization across ... Responsibilities Role Overview This role sits at the intersection of machine learning, data ...
Physics Informed Machine Learning information
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.
- Machine Learning Engineer Biotech
- Senior Machine Learning Engineer Biotech
- Internship Edge Ai Machine Learning
- Freelance Machine Learning Data Annotation
- Virtual Internship Ai Ml
- Edge Ai Machine Learning
- Flexible Machine Learning Engineer Biotech
- Temporary Machine Learning R
- Remote Machine Learning Engineer
- Deep Learning Engineer

Job description
Join us and make YOUR mark on the World!
Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.
Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.
Job Description
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 with physics-based applications in engineering. You will combine existing AI/ML methodologies with state-of-the-art computational modeling and simulation capabilities on high performance computing (HPC) architectures to develop novel application areas within Lawrence Livermore National Laboratory's (LLNL) national security mission space.
You will contribute to research and development in advanced simulation capabilities related to optimizing algorithms and models, surrogate model development, model validation, reliability, uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and programmatic customers to ensure application needs are met. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
These positions will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
- Provide technical leadership and guidance to project teams developing state of the art methods and applying research results to meet programmatic goals, while balancing priorities of customers and partners to ensure deadlines are met.
- Solve abstract and complex problems as required, using in-depth analysis, and drawing from advanced level technical knowledge, best practices, and both routine and innovative techniques and approaches.
- Serve as the primary technical point of contact for program managers internally and at sponsor and partner organizations by sharing relevant advanced level knowledge and providing opinions and recommendations on methodologies, as needed to fulfill deliverables and best meet sponsor needs.
- Utilize advanced level knowledge and skills and apply significant experience in one or more of the following areas of computational science and engineering to new areas at the intersection of artificial intelligence and national security: computational mechanics, chemistry, physics, or materials, nuclear engineering, electrical engineering, non-destructive evaluation, robotics and control, optical systems, high performance computing, or other relevant area of computational science and engineering.
- Develop and apply complex algorithms in one or more of the following machine learning areas/tasks to areas of national security: deep learning, unsupervised/self-supervised learning, representation learning, zero- or few-shot learning, active learning, reinforcement learning, natural language processing, ensemble methods, statistical modeling and inference, performance optimization (scalability, novel hardware, etc.), physics informed machine learning, agentic AI workflows.
- Perform other duties as assigned.
- Establish and implement broad project vision and strategy and influence technical direction and decisions for self and others to drive successful project outcomes.
- Develop novel and innovative Engineering research, technologies, capabilities, and methodologies enabled by the use or integration of applied statistics, machine learning and artificial intelligence, and/or uncertainty quantification.
- Provide subject matter expertise and conduct highly complex and in-depth analysis within one or more areas of machine learning and artificial intelligence, applied statistics, and/or uncertainty quantification.
- Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
- Master's degree in Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science or related technical field or the equivalent combination of education and related experience.
- Advanced level knowledge and significant experience in artificial intelligence, machine learning or data science, and developing applications in one or more of the following areas: mechanical engineering, aerospace engineering, computational mechanics, electrical engineering, applied statistics, uncertainty quantification, or a related technical area.
- Significant experience directing, leading, developing, and executing independent research projects.
- Advanced organizational, verbal and written communication, and interpersonal skills to collaborate effectively in a multidisciplinary team environment, and with subject matter experts, including authoring reports, presenting, and explaining complex technical information.
- Significant experience working effectively in a team environment with multi-disciplinary personnel while managing multiple concurrent tasks and deliverables.
- Subject matter expertise of highly advanced concepts in machine learning or data science and significant experience developing applications in one or more of the following areas: physics, mechanical engineering, aerospace engineering, computational mechanics, electrical engineering, applied statistics, uncertainty quantification, or a related technical area.
- Significant experience and demonstrated ability to successfully lead technical personnel and projects and perform project planning and execution, including applying and developing creative and innovative solutions to highly complex problems.
- Expert communication, facilitation, interpersonal, and collaboration skills necessary to effectively lead a team, present and explain information, and influence and advise senior management and stakeholders, while positively representing the Program and the Laboratory.
- Ability to obtain and maintain Sensitive Compartmented Information (SCI) access which requires U.S. citizenship.
- PhD in Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related technical field, or the equivalent combination of education and related experience.
- Significant experience developing, deploying, and/or utilizing multi-physics simulation codes for massively parallel, high-performance computing architectures utilized by DOE and DoD stakeholders.
$175,530 - $267,060 Annually
$175,530 - $222,564 Annually for the SES.3 level
$210,630 - $267,060 Annually for the SES.4 level
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.
Additional Information
#LI-Hybrid
Position Information
This is a Career Indefinite position, open to Lab employees and external candidates.
Why Lawrence Livermore National Laboratory?
- Included in 2026 Best Places to Work by Glassdoor!
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (*depending on project needs)
- Our values - visit https://www.llnl.gov/inclusion/our-values
Security Clearance
This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.
Pre-Employment Drug Test
External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Wireless and Medical Devices
Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.
How to identify fake job advertisements
Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.
To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf
Equal Employment Opportunity
We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
Reasonable Accommodation
Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.
California Privacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.
Videos To Watch
https://www.youtube.com/watch?v=ITnv867DReU
About LLNLLawrence Livermore National Laboratory
Sourced by ZipRecruiter
Industry
Clean energy services
Company size
5,001 - 10,000 Employees
Headquarters location
Livermore, CA, US