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Modeling and Simulation Engineer

Llnl · Livermore, CA, United States · Hybrid · Active · $146,340–$222,564 / week · SmartRecruiters

Job facts

FieldValue
CompanyLlnl
TitleModeling and Simulation Engineer
Normalized title-
Department / teamScience
LocationLivermore, CA, United States
Work modelHybrid / Hybrid
Employment typeFull Time
Salary$146,340–$222,564 / week
Statusactive
ATS providerSmartRecruiters
Posted / first seen2026-05-20 / 2026-05-31
Changed / last seen2026-05-31 / 2026-06-06

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City jobsActive postings in Livermore.Open
Department jobsActive postings in Science.Open
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Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyLlnl
Source1df6cd9d-2e0a-424c-a75d-264a08f3be51
ATS providerSmartRecruiters

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. We have an opening for a Modeling and Simulation Enginee r 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. 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. 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. 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 targeting high performance computing (HPC) platforms. 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) and 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 analysis of complex mechanical systems subject to dynamic structural and thermal loads with emphasis on high performance computational techniques. Perform other duties as assigned. Additional job responsibilities at the SES.3 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. Provide guidance and mentor junior staff, student fellows, and summer student researchers. Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship. Master’s degree in Mechanical, Aerospace, or Civil Engineering with a focus on structural  mechanics and/or solid mechanics, or the equivalent combination of education and related experience. Comprehensive knowledge of and/or applied experience developing software in Python, C, C++, or similar languages. Proficient 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, as well as part of a multidisciplinary team. Ability to independently gather information from manuals and other documentation to build workflow tools to those programs. Additional qualifications at the SES.3 level Advanced knowledge and operational experience in computational mechanics, including modeling and simulation tools. Advanced knowledge and experience, 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 PhD in Mechanical, Aerospace, or Civil Engineering with a focus on structural  mechanics and/or solid mechanics, or the equivalent combination of education and related experience. Experience conducting computational analysis in a multi-physics context and with software on massively parallel systems. Experience in development of software including testing, documentation, and user support. Experience using and/or developing agentic workflows using Large Language Models in support of computational engineering analysis. Pay Range $146,340 - $222,564 Annually $146,340 - $185,544 Annually for the SES.2 level $175,530 - $222,564 Annually for the SES.3 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. #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 .

Full job record

Job ID4248f29f5f819ddf3ca097c7dfa5f06cec9ceb54
Org IDede835e9-7877-4118-b9da-1ad1bba84e87
Source ID1df6cd9d-2e0a-424c-a75d-264a08f3be51
Board ID1df6cd9d-2e0a-424c-a75d-264a08f3be51
Providersmartrecruiters
Provider Job Key3743990013233126
TitleModeling and Simulation Engineer
Normalized Title
Statusactive
Activeyes
Location TextLivermore, CA, United States
DepartmentScience
Team
Employment Typefull_time
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionCA
CityLivermore
Salary RawJoin 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. We have an opening for a Modeling and Simulation Enginee r 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. 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. 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. 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 targeting high performance computing (HPC) platforms. 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) and 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 analysis of complex mechanical systems subject to dynamic structural and thermal loads with emphasis on high performance computational techniques. Perform other duties as assigned. Additional job responsibilities at the SES.3 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. Provide guidance and mentor junior staff, student fellows, and summer student researchers. Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship. Master’s degree in Mechanical, Aerospace, or Civil Engineering with a focus on structural  mechanics and/or solid mechanics, or the equivalent combination of education and related experience. Comprehensive knowledge of and/or applied experience developing software in Python, C, C++, or similar languages. Proficient 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, as well as part of a multidisciplinary team. Ability to independently gather information from manuals and other documentation to build workflow tools to those programs. Additional qualifications at the SES.3 level Advanced knowledge and operational experience in computational mechanics, including modeling and simulation tools. Advanced knowledge and experience, 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 PhD in Mechanical, Aerospace, or Civil Engineering with a focus on structural  mechanics and/or solid mechanics, or the equivalent combination of education and related experience. Experience conducting computational analysis in a multi-physics context and with software on massively parallel systems. Experience in development of software including testing, documentation, and user support. Experience using and/or developing agentic workflows using Large Language Models in support of computational engineering analysis. Pay Range $146,340 - $222,564 Annually $146,340 - $185,544 Annually for the SES.2 level $175,530 - $222,564 Annually for the SES.3 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. #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 .
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First Seen At2026-05-31 17:34:50Z
Last Seen At2026-06-06 19:34:31Z
Last Checked At2026-06-06 19:34:31Z
Last Changed At2026-05-31 17:34:50Z
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