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Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Data Scientist 2 - Machine Learning for Undersea Acoustics

AI ResearchConfirmed live in the last 24 hours
Full-time$114k - $182.1k /yr

About the Job

Requirements

  • A Bachelor of Science or Bachelor of Arts degree and two years of data science experience, or a Master of Science or Master of Arts degree, or a PhD.
  • Experience developing, training, and evaluating deep learning models for acoustic, sonar, audio, radar, seismic, or related sensor data.
  • Experience implementing, training, and evaluating deep learning models in Python with PyTorch.
  • U.S. citizenship is required.
  • Must be able to obtain and maintain a federal security clearance and meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
  • Must undergo a federal background investigation and pre-employment and random drug testing. Must demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the Questionnaire for National Security Positions.
  • Must obtain and maintain an HSPD-12 Personal Identity Verification credential, or, if a foreign national has not resided in the U.S. for three consecutive years, obtain a favorable Local Site Specific Only federal risk determination to maintain employment.
  • Must disclose any affiliation with the government of a Department of Energy-designated country of risk and recuse from that affiliation or obtain approval from the Department of Energy and Battelle before the first day of employment.

Responsibilities

  • Develop, train, and evaluate deep learning methods for undersea acoustic detection, classification, localization, separation, and related problems.
  • Formulate and investigate research questions through Fourier analysis, signal processing, mathematical analysis, algorithm development, and experiments.
  • Design reproducible experiments and compare methods, examining detection and classification performance, false alarms, robustness to propagation and environmental variability, uncertainty, and computational demands.
  • Analyze and interpret results, document methods and limitations, and communicate findings to project leads, collaborators, and mission partners.
  • Prepare research code and evaluate agentic SI systems with software and machine learning engineers to transition tested methods into usable tools as appropriate.
  • Share technical knowledge across the team and mentor junior colleagues or students as appropriate.
  • Follow applicable quality, safety, and security requirements.

Desired Qualifications

  • Experience developing and evaluating deep learning methods for undersea acoustic detection, classification, localization, or related problems.
  • Experience applying Fourier analysis or other signal processing methods to acoustic or related sensor data.
  • Experience with time-frequency representations or array-processing methods for acoustic analysis.
  • Familiarity with agentic artificial intelligence systems and methods for evaluating their performance and reliability.
  • Familiarity with national security applications of deep learning and undersea acoustics.

About the Company

Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Pacific Northwest National Laboratory (PNNL) conducts scientific research in chemistry, Earth sciences, biology, and data science to advance energy resiliency and national security. Its work spans designing and running experiments, developing models and simulations, and analyzing large data sets to generate knowledge and practical tools that help improve energy systems, environmental stewardship, and security. PNNL differentiates itself through its interdisciplinary approach across multiple scientific domains and its role as a government-funded national lab focused on real-world challenges, enabling collaborations with industry, academia, and government partners. Its goal is to produce actionable scientific insights and deployable technologies that strengthen national resilience and energy infrastructure.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Richland, WA
Founded
1965

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