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Machine Learning Research EngineerLLNL • United States
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Machine Learning Research Engineer

LLNL
  • US
    United States
  • US
    United States
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Über

Company 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 an opening for
Machine Learning Research
experts to join our team and advance the discipline as well as apply cutting edge tools and techniques to some of society's most important problems. You will work with or lead a multi-disciplinary team consisting of machine learning experts, data science practitioners, and domain scientists in areas ranging from fundamental research in machine learning, i.e., AI safety, robustness, uncertainty quantification, or interpretability to applied problems in fields such as high energy density physics, material science, predictive medicine, and treatment discovery. You will also have the opportunity develop and lead independent research thrust and engage with a variety of related research projects in parallel computing, data analysis and visualization, or applied mathematics. This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate.
Essential Duties Research, develop, implement, and evaluate new machine learning techniques for multiple applications in a collaborative scientific environment. Adapt and deploy common machine learning software stack on large-scale high performance computing clusters. Actively participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex problems stemming from national security applications. Adapt current machine learning research to real world applications at scale, with potentially limited and noisy data, with a high consequence of error, and guide the development of practical solutions. Collaborate with a broad spectrum of scientists and engineers, internally and externally, to accomplish research goals. Perform other duties as assigned. In Addition, At SES.3 Level
Provide guidance to subject matter experts in various fields to jointly explore the potential for machine learning research to solve domain specific challenges. Establish future research directions and author grant proposals including presentations to programmatic sponsors and external funding agencies. Lead small to mid-sized research teams in theoretical or applied machine learning in support of one or more mission related scientific applications. Present and disseminate research results at scientific conferences and in peer-reviewed publications. Qualifications
M.S. in Computer Science, Applied Mathematics, Statistics or related field or the equivalent combination of education and related experience. Experience in at least one machine learning research area, such as, foundation models, representation learning, safety & robustness, uncertainty quantification, interpretability, physics-constrained ML, or graph-based learning as demonstrated in software artifacts or publications at high impact AI/AL focused venues. Experience developing, implementing, and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments. Experience in working with diverse teams to solve complex problems and deliver practical solutions. Comprehensive analytical and problem-solving skills necessary to craft creative solutions and solve complex problems. In Addition, at the SES.3 Level
Ph.D. in Computer Science, Applied Mathematics, Statistics or related field or the equivalent combination of education and related experience. Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in high impact AI/ML focused venues, such as, NeurIPS, ICML, ICLR, CVPR, AAAI, AISTATS, UAI, KDD, or JMLR Advanced verbal and written communication skills necessary to interact with a multi-disciplinary research team, author technical and scientific reports and papers, and deliver scientific presentations. Desired Qualifications
Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations or complex workflows. Experience in working with subject matter experts in one or more areas, such as physics, biology, or engineering. Background in statistics, applied mathematics, or related area. Pay Range
$146,340 - $222,564 Annually
$146,340 - $185,544 Annually for the SES.2 job level
$175,530 - $222,564 Annually for the SES.3 job 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
All your information will be kept confidential according to EEO guidelines.
Position Information
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?
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
None required. However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check.
National Defense Authorization Act (NDAA)
The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.
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.
  • United States

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