Chief Engineer – Physical Embodiment of AI

Overview

At PNNL, our core capabilities are divided among major departments that are referred to as directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.

Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.

The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world.

The Physical Detection Systems and Deployment Division, part of the NSD, delivers policy-informed technology solutions by removing barriers to real-world implementation. We strive to understand end‑user environments to transition technology from the developmental stage to deployment. Our diverse expertise in operational systems provides tools, technologies, and approaches for combating a range of threats, both at home and in more than 100 countries around the globe.

We are seeking a Chief Engineer who can bring automation and autonomy to our R&D. A key facet is engineering the physical embodiment of artificial intelligence—translating advanced AI/ML algorithms into robust, fieldable autonomous robotic systems. This role focuses on the hardware implementation of autonomous systems, using advancements in embodied AI to create self‑governing systems that have the capability of adapting without human intervention.

It requires deep expertise in mechanical engineering, robotics integration, autonomy architectures, and physical/digital test infrastructure development.

The selected candidate will lead the design and implementation of integrated robotic platforms, digital twin ecosystems, and laboratory‑scale autonomy testbeds that accelerate deployment across air, ground, maritime, subsea, and space domains. This role bridges mechanical design, embedded systems, autonomy software, simulation environments, and sponsor‑driven mission needs.

• Define and execute the technical vision for AI‑enabled autonomous systems with an emphasis on physical system realization and field transition
• Lead development of integrated autonomy architectures spanning mechanical systems, sensing, controls, embedded compute, and AI/ML stacks
• Translate sponsor mission needs into engineered robotic systems and deployable autonomy capabilities
  Establish digital twin and physical prototyping capabilities that accelerate system validation and transition

• Lead mechanical design and integration of robotic platforms including:
  • Structural systems
  • Actuation and mobility subsystems
  • Power distribution and thermal management
  • Payload integration and sensor mounting architectures
• Engineer robotic embodiments capable of operating in austere and mission‑relevant environments
• Develop modular hardware architectures to enable rapid prototyping and mission reconfiguration
• Oversee fabrication, assembly, integration, and validation of robotic platforms
• Conduct system‑level trade studies balancing weight, power, compute, and autonomy performance

• Design and implement end‑to‑end autonomy stacks integrating:
  • Perception (EO/IR, LiDAR, IMU, multi‑modal fusion)
  • State estimation and navigation
  • Planning and decision‑making
  • Controls and low‑level actuation
• Deploy advanced ML architectures onto edge and embedded hardware platforms (e.g., NVIDIA Jetson‑class devices)
• Optimize models for real‑time, low‑latency operation within power‑ and compute‑constrained robotic systems
• Develop scalable MLOps pipelines tailored for robotics environments

• Architect and implement high‑fidelity digital twin environments for robotic systems using simulation platforms such as Omniverse, Isaac Sim, Unreal, or equivalent
• Establish software‑in‑the‑loop (SIL), hardware‑in‑the‑loop (HIL), and human‑in‑the‑loop testing frameworks
• Design hybrid…