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 committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory’s mission.
$123,960.00 - $190,932 Annually
$123,960.00 - $159,168 Annually for the SES.2 level
$148,650.00 - $190,932 Annually for the SES.3 level
Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.
We have multiple openings for Computational Optimization Engineers to conduct basic and applied research in the simulation and design optimization of complex nonlinear physical systems. You will work collaboratively and independently to extend and develop physics-based design optimization tools. The team is working to both extend the state-of-the-art and support Laboratory missions. These positions are in the Computational Engineering Division (CED).
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.
- Develop and implement HPC algorithms for the simulation and design optimization of systems governed by nonlinear, transient, multiscale, and multiple physical phenomena that include uncertainties in loadings, geometry and material properties;
- Contribute to solving challenging design optimization problems;
- Develop and implement continuum simulations to solve the governing partial differential equations that model the physical systems necessary for their design optimization;
- Document research through presentations and peer-reviewed journal articles and contribute to identifying future research directions and proposals that will secure future projects in the field;
- Perform other duties as assigned.
Additional job responsibilities, at the SES.3 level
- Guide the establishment of future research directions and influence proposals that will secure future projects in the field;
- Identify and solve challenging design optimization problems highlighting capabilities and establishing credibility with mission customers.
- Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship;
- Master’s Degree in Engineering, Mathematics, Computational Science, or a related field, or the equivalent combination of education and related experience;
- Knowledge of shape/topology optimization techniques and algorithms;
- Experience developing simulation tools using continuum approaches (e.g., finite elements) for physical systems;
- Experience programming in C/C++/FORTRAN and scripting languages including Python/M;
- Comprehensive experience with design sensitivity analysis via direct (forward) and adjoint (backward) methods;
- Knowledge of massively-parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA;
- Proficient verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers.
Additional qualifications at the SES.3 level
- Advanced knowledge of shape/topology optimization techniques and algorithms;
- Significant experience developing simulations tools using continuum approaches (e.g., finite elements) for physical systems and advanced programming skill in C/C++/FORTRAN and scripting languages including Python/;
- Significant experience with design sensitivity analysis via direct (forward) and adjoint (backward) methods;
- Significant experience with massively-parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA;
- Advanced verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers.
Qualifications We Desire
- PhD in Engineering, Mathematics, Computational Science, or a related field;
- Exposure to machine learning and other data science techniques.
All your information will be kept confidential according to EEO guidelines.
This is a Flexible Term appointment, which is for a definite period not to exceed six years. If final candidate is a Career Indefinite employee, Career Indefinite status may be maintained (should funding allow).
Why Lawrence Livermore National Laboratory?
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.
Beware of Fraudulent Job Postings. LLNL’s hiring practices:
- Never requires job applicants to pay an application/training fee or submit personal documents like bank account details, passport number, Social Security number, tax forms or credit card information as part of the application process;
- For interviews and to be granted access to a Federal facility, a LLNL employee will contact you directly to collect visa, passport number, and/or Social Security number. To vet the authenticity of the employee please have them provide you their name and phone number and verify at people.llnl.gov;
- Involves at least one interview (virtual or in-person) and never interviews job applicants through chat platforms such as Google Hangouts, or via correspondence through text and instant messaging systems;
- Only sends email communications to job applicants from domain “@llnl.gov” or via their applicant tracking system, email@example.com . Occasionally LLNL uses third-party vendors that will contact you about job opportunities. If a recruiter contacts you to apply, you will always be directed to our career page to apply through our career site;
- Encourages all applicants to visit LLNL’s careers page at www.llnl.gov/join-our-team/careers if they saw the job posting on another site prior to applying to ensure the job posting is accurate and valid.
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.
We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.
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.