Reinforcement Learning Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

We have multiple openings for Reinforcement Learning Research Staff Members to conduct basic and applied research in Reinforcement Learning for (1) optimal sequential decision-making under uncertainty and/or (2) symbolic optimization, with real-world applications spanning healthcare, biosecurity, and national security. These positions are in the Computational Engineering Division (CED), within the Engineering 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.

In this role you will

  • Conduct research and development for controlling simulations and/or symbolic optimization using approaches such as deep reinforcement learning, bandit optimization, evolutionary algorithms, model-based methods, and stochastic control.
  • Implement and perform computational analysis in one or more of the above areas.
  • Document research, write technical reports or papers in peer-reviewed journals, and present results within the relevant community.
  • Interact with a broad spectrum of scientists from internal and external communities.
  • Mentor student research interns.
  • Perform other duties as assigned.

Additional job responsibilities, at the SES.3 level

  • Design, implement, and analyze reinforcement learning algorithms for optimal decision-making under uncertainty and/or symbolic optimization.
  • Guide and lead the completion of projects and contribute to the development of organizational objectives and fully function as a team member on multidisciplinary teams.
  • Interact with professional colleagues, mid-level internal management, and sponsor representatives on matters requiring coordination across organizational lines. Represent the organization as the primary technical contact on tasks and projects. Serve on internal technical/advisory committees and may serve on external committees.

  • Master's degree in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operations Research or related field, or the equivalent combination of education and related experience.
  • Comprehensive knowledge and experience in reinforcement learning, active learning, or stochastic control algorithms.
  • Proficiency in one or more of the following machine learning areas: deep learning, unsupervised feature learning, multimodal learning, and probabilistic graphical models.
  • Experience in the broad application of two or more higher-level programming languages such as Python, Java, Matlab, R or C/C++.
  • Experience with one or more deep learning libraries such as TensorFlow, Keras, Caffe or Theano, and experience with one or more deep reinforcement learning libraries such as rllab, keras-rl or OpenAI Gym.
  • Demonstrated proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.
  • Demonstrated initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Additional qualifications at the SES.3 level

  • Significant experience with deep reinforcement learning algorithm development and with deep learning model development using TensorFlow, Keras, Caffe or Theano.
  • Significant advanced application and development in two or more higher-level programming languages such as Python, Java, Matlab, R or C/C++.
  • Advanced verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information and provide advice to management. Experience in writing proposals.

Qualifications We Desire

  • PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operation Research or related field.
  • Strong math background and experience with mathematical formulations of complex systems.
  • Experience with a variety of deep reinforcement learning algorithms including experience with variational Bayesian methods, nonparametric Bayesian methods, and multi-agent systems. In addition, experience with utilizing simulation to model and analyze complex systems and experience with parallel computing and/or GPU computing.

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.

COVID-19 Vaccination Mandate

LLNL demonstrates its commitment to public safety by requiring that all new Laboratory employees be immunized against COVID-19 unless granted an accommodation under applicable state or federal law. This requirement will apply to all new hires including those who will be working on site, as well as those who will be teleworking.

Security Clearance

LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory. Some positions will require a DOE L or Q clearance (please reference Security Clearance requirement above). If you are selected and a clearance is required, we will initiate 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. An L or Q clearance requires U.S. citizenship. For additional information please see DOE Order 472.2.

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.

LLNS is committed to offering reasonable accommodations during the application and recruiting processes due to a disability. If you need assistance or an accommodation due to a disability, please submit a request via our online form.

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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.


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 essential to innovation and creativity for continued success of the Laboratory's mission.

"},"jobDescription":{"title":"Job Description","text":"

We have multiple openings for Reinforcement Learning Research Staff Members to conduct basic and applied research in Reinforcement Learning for (1) optimal sequential decision-making under uncertainty and/or (2) symbolic optimization, with real-world applications spanning healthcare, biosecurity, and national security. These positions are in the Computational Engineering Division (CED), within the Engineering 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.

In this role you will

  • Conduct research and development for controlling simulations and/or symbolic optimization using approaches such as deep reinforcement learning, bandit optimization, evolutionary algorithms, model-based methods, and stochastic control.
  • Implement and perform computational analysis in one or more of the above areas.
  • Document research, write technical reports or papers in peer-reviewed journals, and present results within the relevant community.
  • Interact with a broad spectrum of scientists from internal and external communities.
  • Mentor student research interns.
  • Perform other duties as assigned.

Additional job responsibilities, at the SES.3 level

  • Design, implement, and analyze reinforcement learning algorithms for optimal decision-making under uncertainty and/or symbolic optimization.
  • Guide and lead the completion of projects and contribute to the development of organizational objectives and fully function as a team member on multidisciplinary teams.
  • Interact with professional colleagues, mid-level internal management, and sponsor representatives on matters requiring coordination across organizational lines. Represent the organization as the primary technical contact on tasks and projects. Serve on internal technical/advisory committees and may serve on external committees.
"},"qualifications":{"title":"Qualifications","text":"
  • Master's degree in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operations Research or related field, or the equivalent combination of education and related experience.
  • Comprehensive knowledge and experience in reinforcement learning, active learning, or stochastic control algorithms.
  • Proficiency in one or more of the following machine learning areas: deep learning, unsupervised feature learning, multimodal learning, and probabilistic graphical models.
  • Experience in the broad application of two or more higher-level programming languages such as Python, Java, Matlab, R or C/C++.
  • Experience with one or more deep learning libraries such as TensorFlow, Keras, Caffe or Theano, and experience with one or more deep reinforcement learning libraries such as rllab, keras-rl or OpenAI Gym.
  • Demonstrated proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.
  • Demonstrated initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Additional qualifications at the SES.3 level

  • Significant experience with deep reinforcement learning algorithm development and with deep learning model development using TensorFlow, Keras, Caffe or Theano.
  • Significant advanced application and development in two or more higher-level programming languages such as Python, Java, Matlab, R or C/C++.
  • Advanced verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information and provide advice to management. Experience in writing proposals.

Qualifications We Desire

  • PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operation Research or related field.
  • Strong math background and experience with mathematical formulations of complex systems.
  • Experience with a variety of deep reinforcement learning algorithms including experience with variational Bayesian methods, nonparametric Bayesian methods, and multi-agent systems. In addition, experience with utilizing simulation to model and analyze complex systems and experience with parallel computing and/or GPU computing.

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Reinforcement Learning Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

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