Join us and make YOUR mark on the World!
Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2020 Best Places to Work by Glassdoor!
We have multiple openings for machine learning 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 robustness, uncertainty quantification, or interpretability in machine learning to applied problems in fields such as high energy density physics, material science, cancer biology, and traumatic brain injury. 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.
- Research, develop, implement, and evaluate new machine learning techniques.
- Provide guidance to subject matter experts in various fields to jointly explore the potential for machine learning research to solve domain specific challenges.
- 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.
- Present and disseminate research results at scientific conferences and in peer-reviewed publications.
- Establish future research directions and author grant proposals including presentations to programmatic sponsors and external funding agencies.
- 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.4 Level
- Establish independent research thrusts through strategic engagements with internal and external sponsors.
- Lead mid- to large-sized research teams in theoretical or applied machine learning in support of one or more mission related scientific applications.
- Provide strategic guidance to LLNL management and demonstrate technical leadership in the machine learning research community.
- Ph.D. in Computer Science, Applied Mathematics, Statistics or related field or the equivalent combination of education and related experience.
- Significant experience in at least one machine learning research area, such as computer vision, natural language processing, robust optimization, interpretability, or graph-based learning.
- Significant experience in modern machine learning environments (i.e., TensorFlow, Kieras, PyTorchand related data ecosystems.
- Significant experience in working with diverse teams to solve complex problems and deliver practical solutions.
- 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.
- Comprehensive analytical and problem-solving skills necessary to craft creative solutions and solve complex problems.
In Addition at the SES.4 Level
- Substantial record of sustained program development and strategic engagement in fields related to machine learning.
- Substantial technical leadership in fields related to machine learning.
- Expert verbal and written communication and interpersonal skills necessary to effectively collaborate with internal and external teams to present and explain technical information and to advise senior management and external sponsors
- Experience with C/C++ and application development including workflow tools, batch submission, and parallel computing.
- Experience in working with subject matter experts in one or more areas, such as physics, biology, and engineering.
- Background in statistics, applied mathematics, or related area.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to 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.
Security Clearance: This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on theparticular assignment.
If you are selected and a security clearance is required, wewillinitiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. L and Q-level clearances require U.S. citizenship. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted.
If no security clearance is required, but 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. (This process does not apply to foreign nationals.)
Note: This listing has multiple openings; to be filled as Career Indefinite or as At Will appointments. Lab employees and external candidates may be considered for these positions.
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applyingcutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $2.3 billion, employing approximately 6,900 employees.
LLNL is an affirmative action/ equal opportunity employer. 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, protected veteran status, age, citizenship, or any other characteristic protected by law.