LLNL

Research

MachineLearningResearchEngineer

$215–320k ~AI est. Livermore, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“Machine Learning Research Engineer at LLNL. Skills: Machine learning research, Applied machine learning, Scientific computing. Research machine learning techniques. Develop machine learning techniques”

Industry & Context.

Research
Problems you'll solve

Analytical; Problem-solving

What They're Looking For.

Must Have

M.S. in Computer Science, Applied Mathematics, or Statistics, Experience in at least one ML research area, Experience developing advanced ML models, Experience working with diverse teams, Comprehensive analytical skills

Nice to Have

Ph.D. in Computer Science, Applied Mathematics, or Statistics, Demonstrated research productivity, Advanced verbal and written communication skills, Experience with high-performance computing, Experience with GPU programming, Experience with parallel programming, Experience with cloud computing, Experience running numerical simulations, Experience with complex workflows, Experience with physics, Experience with biology, Experience with engineering, Background in statistics, Background in applied mathematics

What You'll Do.

Research machine learning techniques

Develop machine learning techniques

Implement machine learning techniques

Evaluate machine learning techniques

Adapt machine learning software

Deploy machine learning software

Participate in defining efforts

Plan experimental efforts

Formulate experimental efforts

Adapt ML research to applications

Guide development of practical solutions

Collaborate with scientists

Collaborate with engineers

Provide guidance to experts

Explore potential for ML research

Establish research directions

Author grant proposals

Present research results

Disseminate research results

How You'll Work.

Team & Collaboration

Multi-disciplinary teams; Cross-functional teams

Communication Scope

Scientific presentations; Technical reports; Scientific papers

Full Job 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. 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. * Adap

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