Machine Learning Researcher

Location
Livermore, California, US
Salary
Competitive
Posted
Jun 10, 2024
Posting live until
Sep 08, 2024
Ref
REF6119X

Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory’s mission.

Pay Range

$132,810.00 - $170,556.00 Annually for the SES.2 level

$159,330.00 - $204,636.00 Annually for the SES.3 level

Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

Job Description

We have multiple openings for Machine Learning Researchers 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.

This position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.

You will

  • 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.

Additional job responsibilities, at the 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.
  • Proficient 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
  • Broad 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.

Additional qualifications 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.

Qualifications We Desire

  • 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.

Additional Information

Why Lawrence Livermore National Laboratory?

  • Included in 2021 Best Places to Work by Glassdoor!
  • Work for a premier innovative national Laboratory
  • Comprehensive Benefits Package
  • Flexible schedules (*depending on project needs)
  • Collaborative, creative, inclusive, and fun team environment

Learn more about our company, selection process, position types and security clearances by visiting our Career site.

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.

Equal Employment Opportunity

LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. 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.

If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form.

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.

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?

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. (This process does not apply to foreign nationals.)

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.

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.

We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.

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.