Machine Learning Research Engineer

Mid-Senior Level | Full-time
Information Technology/Computing | Livermore, CA | 06/01/2026
Reference #: REF
8418D
Job Code: SES.
2 Science & Engineering MTS 2 / SES.
3 Science & Engineering MTS 3
Organization: Computing
Position Type: Flexible Term
Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable Apply Now

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 to 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.

• 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.
• 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.

• 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, Tensor Flow, 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,…