Modeling and Simulation Engineer

Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory’s mission.

Pay Range

$117,180 - $178,392

$117,180 - $148,608 Annually for the SES.1 level

$140,700 - $178,392 Annually for the SES.2 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.

Job Description

We have an opening for a Modeling and Simulation Engineer within the Analytical Capability Group (ACG).  ACG supports LLNL weapon system design and assessments with modeling and simulation in-house tool development. You will perform code development work focusing on a Python modeling environment (a collection of libraries and applications) that interfaces with multiple engineering solvers and post-processing applications. This will involve developing methods and tools to streamline the end-to-end engineering analysis workflow, which includes Product Lifecycle Management and Simulation Process Data Management platform integration, mesh generation, model preprocessing, job monitoring, simulation postprocessing and visualization, and report generation. This work additionally prepares for integration within a multi-agent large language model framework to accelerate the end-to-end analysis process. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

This position will be filled at either level based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level.

In this position you will

• Contribute to the development of software tools for managing Finite Element Analysis (FEA) and other numerical modeling workflows including preprocessing, post-processing, and data management.
• Provide support to engineering analysts, which may include debugging problems, providing alternative solutions, and quickly implementing new features.
• Participate in the integration of engineering analysis tools with other frameworks supporting LLNL’s Digital Transformation objectives, as well as Artificial Intelligence (AI) or Machine Learning (ML) integration within the end-to-end engineering analysis process.
• Document analyses, methods, and implementation activities and processes in both informal and formal reports and presentations.
• Share relevant knowledge, analysis, and recommendations in collaboration with internal and external scientists, engineers, mathematicians, and computer scientists through reviews and working groups. Interface with other development teams working on tools that support analysis workflows.
• Perform other duties as assigned.

Additional job responsibilities at the SES.2 level 

• Exercise independent judgment to determine technical objectives, criteria, and techniques to execute the appropriate technical approaches, obtain and analyze results, provide solutions for a diverse range of moderately complex problems, and to satisfy project deliverables.
• Utilize comprehensive engineering knowledge and apply relevant professional experience, broad technical concepts and techniques, and best practices to provide input on technical issues for specific tasks and projects, resolve technical and programmatic issues, and contribute to the successful completion  project and organizational goals.
• Coordinate and contribute to the execution of engineering testing and data analysis and assist in preparing technical reports, documentation, and presentations.
• May provide guidance and mentor student fellows and summer student researchers.

Qualifications

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Bachelor’s degree in engineering, computational science, applied math, physics, or related technical field, or the equivalent combination of education and related experience.
• Demonstrated ability, fundamental knowledge of,  and/or applied experience writing programming in Python, C, C++, or similar languages.
• Fundamental knowledge and/or exposure to and experience in computational structural mechanics, material failure, dynamics, heat transfer, or implicit thermo-mechanical response of systems.
• Sufficient  interpersonal, written, and verbal communication skills necessary to effectively interact and collaborate in a multidisciplinary team environment and to present and explain technical information.
• Ability to work independently to complete assignments, while adhering to defined practices and procedures.

Additional qualifications at the SES.2 level 

• Comprehensive knowledge, full understanding of, and operational experience in computational mechanics, including modeling and simulation tools.
• Comprehensive knowledge of, broadly applied experience with, and demonstrated proficient skills writing in one or more programming languages such as Python, C, C++, or similar languages.
• Demonstrated ability to meet schedule and deliverables for multiple concurrent projects with a range of priorities, and to reprioritize and change programmatic direction depending on emergent requests and needs.
• Experience preparing and conducting testing and data analysis, as well as performing and completing documentation, tracking, and reporting of action items.

Qualifications We Desire

• Master’s degree in engineering, computational science, applied math, physics, or related field.
• Experience conducting computational analysis in a multi-physics context and with software on massively parallel systems.
• Experience in development of software including testing and documentation.
• Experience with custom workflows using Large Language Models.
• Experience with object oriented programing (OOP).
• Experience in user support.

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 2025 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.