Lawrence Livermore National Laboratory

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Reinforcement Learning Researcher

Location:  Livermore, CA
Category:  Science & Engineering
Organization:  Engineering
Posting Requirement:  External Posting
Job ID: 106142
Job Code: Science & Engineering MTS 2 (SES.2) / Science & Engineering MTS 3 (SES.3)
Date Posted: September 25 2019

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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 researchers to conduct basic and applied research in Reinforcement Learning for optimal sequential decision-making under uncertainty, on real-world problems in healthcare, cyber security, and national security applications. These positions are in the Computational Engineering Division (CED) within the Engineering Directorate.

These positions will be filled at either the SES.2 or SES.3 level depending on your qualifications. Additional job responsibilities (outlined below) will be assigned if you are selected at the higher level.

Essential Duties
- Conduct research and development for controlling simulations 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.
- Provide informal mentoring to student research interns.
- Perform other duties as assigned.
In Addition at the SES.3 Level
- Design, implement, and analyze reinforcement learning algorithms for optimal decision-making under uncertainty.
- Guide 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 relevant 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.
- Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.
- Initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.
In Addition 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.

Desired Qualifications
- 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. Experience with utilizing simulation to model and analyze complex systems and experience with parallel computing and/or GPU computing.

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 the particular assignment.

If you are selected and a security 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.  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; these are Career Indefinite positions. Lab employees and external candidates may be considered for these positions.

About Us

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 applying cutting-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 $2.1 billion, employing approximately 6,800 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.