Data Scientist - Field Robotics and AI

Overview

At Pacific Northwest National Laboratory (PNNL), our core capabilities are organized into major departments known as directorates. The Energy and Environment Directorate delivers science and technology solutions for the nation’s biggest energy and environmental challenges. Our Coastal Sciences Division, part of this directorate, is based at PNNL–Sequim and focuses on marine and coastal resources, environmental chemistry, water resources modeling, marine engineering, environmental modeling and monitoring, and national security.

The division is a leading organization in sustainable ocean energy, mitigation of climate impacts on marine resources, and protection of coastal environments.

Design, develop, and implement methods, processes, and systems to analyze diverse data. Apply knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, and generate discoveries from structured and unstructured data. Produce solutions driven by exploratory data analysis from complex, high‑dimensional datasets. Design, develop, and evaluate predictive models and advanced algorithms that optimize value extraction from data, demonstrating the ability to transfer skills across domains.

Work with the Coastal Sciences team, a 80‑member multidisciplinary group focused on field robotics, sensing, and AI.

Lead projects that involve:

• On‑board marine robotic autonomy and data processing software stack development for autonomous surface vessels (ASVs), autonomous underwater vessels (AUVs), and autonomous aerial vehicles (AAVs).
• Real‑time multi‑sensor array, networking, interfacing, and data processing development for video, acoustic, and novel sensor 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 the cloud.
• Design, configuration, and management of heterogeneous clusters of computing resources for lab and deployment needs.

The right candidate will be excited to join an interdisciplinary team, be an adaptive generalist with a strong background in software engineering, robotics, and machine learning, and possess excellent communication and teaching skills. Experience in the maritime domain and the ability to support marine deployments in both office and field settings are essential.

• BS/BA and 2 years of relevant experience
• MS/MA
• PhD

• Graduate degree in Computer Science, Robotics, Electrical/Computer Engineering or significant experience illustrating an ability to perform and lead 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 comfort with Linux computer systems and the ability to set them up and configure them.
• Familiarity with sensor hardware, communication protocols, and networking systems.
• Experience with PyTorch, Tensor Flow, or similar auto‑grad libraries and a clear understanding of machine learning fundamentals.
• Knowledge of common statistical methods, probability theory, graduate‑level linear algebra, and control theory.
• Experience creating, developing within, and deploying containerized software environments using Docker, Podman, or similar engines.
• Experience configuring and using modern dev‑ops tools and pipelines.
• Experience designing, building, and programming robotic systems.
• Research experience culminating in publications, talks, or other technical products.
• Experience with ROS2, Gazebo, and Isaac Sim.
• Experience in marine or field robotics and familiarity with common robotics platforms in these domains.
• Experience with embedded systems, micro‑controllers, single‑board computers, sensor integration, and SWAP‑constrained systems.
• Experience with common marine sensors and the oceanographic properties they measure.
• Experience with IoT…