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: 103478
Job Code: Science & Engineering MTS 1 (SES.1) / Science & Engineering MTS 2 (SES.2)
Date Posted: March 06 2018

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Science and Technology on a Mission!

For more than 60 years, the Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place.

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

Essential Duties
- Work under general direction to implement algorithms for controlling simulations using approaches such as deep reinforcement learning, bandit optimization, evolutionary algorithms, model-based methods, and stochastic control.
- Maintain a close interface with programmatic and project elements to ensure effective teamwork.
- Document efforts in technical reports and presentations.
- Contribute to the fulfillment of technical projects and organizational objectives as a member of a team.
- Perform other duties as assigned.
In Addition at the SES.2 Level
- Research, develop, and apply solutions to moderately complex machine learning problems of programmatic interest.
- Publish papers in peer-reviewed journals and present results at scientific meetings and conferences.

Qualifications
- Bachelor’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.
- Working 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.
- Fundamental knowledge 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 ability to work independently under general direction within the scope of an assignment and use independent judgment in determining methods, techniques, and evaluation criteria.
- Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information.
In Addition at the SES.2 Level
- 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 with deep reinforcement learning algorithm development and with deep learning model development using TensorFlow, Keras, Caffe or Theano.
- Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information and provide advice to management.

Desired Qualifications
- PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operations Research or related field.

Pre-Employment Drug Test:  External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.
 

Security Clearance:  This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the location of the 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.

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 $1.8 billion, employing approximately 6,500 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.

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