Physics-Informed Machine Learning Specialist

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 Laboratorys (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.

Additional job responsibilities at the SES.4 level 

• 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.

Qualifications :

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Masters 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.

Additional qualifications at the SES.4 level 

• 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.

Qualifications We Desire

• 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.

Pay Range

$175530 - $267060 Annually

$175530 - $222564 Annually for the SES.3 level
$210630 - $267060 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 employees 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  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.

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  Remote Work :

  No

  
  

  Employment Type :

  Full-time