Machine Learning Researcher

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 a Machine Learning and Data Analysis expert 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 and lead multi-disciplinary teams consisting of machine learning experts, data science practitioners, analysis experts, and domain scientists in areas ranging from fundamental research in machine learning, development, deployment, and performance optimization of large scale AI models, to applied AI and analysis problems in fields such as high energy density physics, material science, predictive medicine, and treatment discovery. You will also have the opportunity develop research strategies in these areas 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

• Establish independent research thrusts through strategic engagements with internal and external sponsors.
• Lead mid- to large-sized research teams in applied machine learning and data analysis in support of one or more mission related scientific applications.
• Provide strategic guidance to LLNL management and demonstrate technical leadership in the research community.
• Research, develop, implement, and evaluate new machine learning and data analysis techniques for multiple applications in a collaborative scientific environment.
• 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.
• 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.5 Level

• Provide scientific and technical direction for large projects and programs.
• Support lab leadership in attracting retaining projects, programs, funding, and staff.
• Engage and influence senior management, policy makers, and external sponsors.

Qualifications

• Ph.D. in Computer Science, Applied Mathematics, Statistics or related field or the equivalent combination of education and related experience.
• 8+ years of experience post PhD in research in machine learning and data analysis
• Significant experience in foundational or applied machine learning research area and large scale data analysis.
• Experience independently developing, implementing, and applying advanced statistical tools, machine learning models, and data analysis algorithms using modern software libraries such as C++, PyTorch, TensorFlow, or similar as evidence through medium to large scale models, applications, and experiments.
• Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in high impact venues, such as, IEEE Transactions, NeurIPS, ICML, MLST, PNAS, etc.  
• Significant experience in working with diverse teams to solve complex problems and deliver practical solutions.
• Substantial record of sustained program development and strategic engagement in fields related to machine learning and data analysis.
• Experience leading research teams in achieving long term objectives and delivering solutions.
• Expert verbal and written communication and interpersonal skills necessary to effectivel