Pacific Northwest National Laboratory Jobs

Job Information

Pacific Northwest National Laboratory AI Scientist in SEATTLE, Washington

Organization and Job ID

Job ID: 310794

Directorate: National Security Directorate

Division: Computing & Analytics

Group: Applied Statistics & Computational Modeling

Job Description

AI is poised to rapidly transform how scientific research and national security missions are carried out. PNNL is looking for candidates with strong AI/ML background and a passion for scientific research that are ready to apply their knowledge across a diverse array of science and national security application spaces. Candidates will work in the areas of multi-agent systems, machine learning, and sequential decision making under uncertainty with applications in cybersecurity, cyber-physical security, scientific computing, material sciences, and computational biology.

To contribute to these research efforts, a successful candidate will have technical expertise in one or more areas including reinforcement learning, deep learning, optimization and control of dynamical systems, foundational game theory, network and decision science, Bayesian methods, causal reasoning, natural language processing, and uncertainty quantification. In addition, the candidate should be proficient in latest programming languages and software tools for machine learning and optimization. The candidate is expected to collaborate effectively within multi-disciplinary research teams at PNNL.

The hiring level will be determined based on the education, experience, and skill set of the successful candidate based on the following:

Level I : Expected to contribute professionally, building a professional reputation for technical expertise. Fully applying and interpreting standard theories, principles, methods, tools, and technologies.

Level II : Leads specific tasks of the project to meet scope, schedule, and budget. Expected to contribute professionally, building a professional reputation for technical expertise. Fully applying and interpreting standard theories, principles, methods, tools, and technologies. Contributes technical content to proposals and develops business through excellent project performance.

Minimum Qualifications

  • BS/BA with 0-1 years of experience; MS/MA with 0-1 years of experience

Preferred Qualifications

  • BS/BA with 2 years of experience; MS/MA or Ph.D. in Computer Science/Operations Research/Electrical Engineering or other related fields

  • Methodological interests in the areas of reinforcement learning, optimization, network and decision science, and game theory. Experience with development of reinforcement learning algorithms for dynamical control systems

  • Experience with mathematical modeling for cybersecurity problems

  • Proficient in PyTorch or TensorFlow with some experience of deploying models in Open AI-Gym

  • Proficient in programming languages, such as Python, Julia, C, C++, Java

  • Experience with optimization solvers for convex and non-convex problems

Equal Employment Opportunity

Battelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov.

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from participation in certain foreign government talent recruitment programs. If you are offered a position at PNNL and are currently a participant in a foreign government talent recruitment program you will be required to disclose this information before your first day of employment.

Directorate: National Security

Job Category: Scientists/Scientific Support

Group: Appld Stats & Comp Modeling

Opening Date: 2020-04-28

Closing Date: 2020-08-26

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