Data Scientist - Field Robotics and AI

Overview

Pacific Northwest National Laboratory (PNNL) is a national laboratory that specializes in scientific research and technology solutions across multiple directorates. The Coastal Sciences Division (CSD), part of the Energy and Environment Directorate, is headquartered at PNNL-Sequim on Washington State’s Olympic Peninsula. The division focuses on marine and coastal resources, environmental chemistry, water resources modeling, marine engineering, environmental modeling and monitoring, and national security.

It is an emerging leader in sustainable development of ocean energy, mitigating long‑term human impacts, and protecting coastal environments from security threats.

• Designs, develops, and implements methods, processes, and systems to analyze diverse data.
• Applies knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data.
• Produces solutions driven by exploratory data analysis from complex and high‑dimensional datasets.
• Designs, develops, and evaluates predictive models and advanced algorithms that lead to optimal value extraction from the data.
• Demonstrates ability to transfer skills across application domains.

• Onboard marine robotic autonomy and data processing software stack development for in‑house autonomous surface vessels (ASVs), autonomous underwater vessels (AUVs), and autonomous aerial vehicles (AAVs).
• Real‑time multi‑sensor sensor array, networking, interfacing, and data processing development for video, acoustic, and other novel sensor array systems.
• Deep learning and AI‑based detection, tracking, and reasoning on diverse marine and field sensor streams.
• Development of robust, communication pipelines both on‑prem, in‑field, and to cloud.
• Design, configuration, and management of heterogeneous clusters of computing resources for various lab and deployment needs.

• Excited to be a part of a close‑knit and interdisciplinary team, and to represent and share unique domain expertise.
• Adaptable generalist with a deep background in software engineering, robotics, and machine learning, strong communication skills, and a teaching spirit.
• Excited to work in the maritime domain, support marine deployments, and work with colleagues both in office and in the field.
• Driven to identify and pursue novel science questions and share the results of research with the broader community.
• Can handle working on multiple projects simultaneously, navigate complex organizational processes, and maintain long‑term vision despite short‑term ambiguity.

Minimum Qualifications: BS/BA and 2 years of relevant experience — OR — MS/MA — OR — PhD.

Preferred Qualifications:

• A graduate degree in Computer Science, Robotics, Electrical/Computer Engineering or significant experience of leading research in robotics, software engineering, and deep learning without an applicable degree.
• Proven software development experience in professional and research settings within large, multi‑developer code bases.
• Object‑oriented programming experience in Python, C/C++, and/or Rust.
• Deep knowledge of and demonstrated comfort with Linux computer systems and the ability to set up and configure them.
• Familiarity with common sensor hardware, communication protocols, and networking systems.
• Experience with PyTorch, Tensor Flow, or another auto‑grad library, understanding of the fundamentals of machine learning and deep learning, and applying cutting‑edge deep learning models to real‑world data.
• Knowledge of common statistical methods, probability theory, graduate‑level linear algebra, and control theory.
• Experience creating, developing within, and deploying containerized software environments using tools such as Docker, Podman, or other containerization engines.
• Experience configuring and using modern dev‑ops tools and pipelines.
• Experience designing, building, and programming robotic systems.
• Research experience culminated in publications, talks, or other…