Chief Engineer – Physical Embodiment of AI

Overview

At PNNL, our core capabilities are divided among major departments that we refer 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.
• Oversee 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, and 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 physical/digital experimentation workflows linking lab hardware with simulation-based validation.
• Develop robotic laboratories and autonomy experimentation facilities capable of supporting multi-domain platforms.
• Build data pipelines for synchronized physical and simulated experimentation.
• Enable rapid iteration between simulated and real-world deployments.

• Lead requirements definition, verification, validation, and accreditation (VV&A).
• Execute Test & Evaluation (T&E) for fieldable systems under sponsor constraints.
• Conduct performance characterization of mechanical, electrical, and autonomy subsystems.
• Ensure systems meet safety, cybersecurity, and compliance requirements for defense applications.
• Lead integration and demonstration efforts during on-site DoD test events.

• Shape…