Lead Engineer – Physical Embodiment of AI

Pacific Northwest National Laboratory•Richland, WA

18h•$148,000 - $233,900•Onsite

About The Position

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 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.

Requirements

BS/BA and 7+ years of relevant work experience -OR- MS/MA and 5+ years of relevant work experience -OR- PhD with 3+ year of relevant experience
U.S. Citizenship
Ability to obtain and maintain a federal security clearance
Must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).

Nice To Haves

Demonstrated experience designing and deploying integrated robotic systems
Strong background in mechanical system design for mobile or fieldable platforms
Experience developing and validating digital twins of physical robotic systems
Expertise in autonomy stack integration across hardware and software layers
Experience with embedded systems and edge AI optimization
Proficiency in Python and modern C++
Experience with ROS/ROS2 and robotics middleware
Experience building robotic laboratories or autonomy experimentation facilities
Experience implementing SIL/HIL frameworks
Experience integrating COTS embedded compute platforms (Jetson, Raspberry Pi, M.2 accelerators)
Experience generating and scaling datasets through simulation engines (Omniverse, Isaac Sim, Unreal, Blender)
Sustained experience in autonomous systems R&D
Experience transitioning research systems into operational environments
Record of peer-reviewed publications or juried conference presentations
Experience working with federal sponsors, particularly within defense or national security missions
Knowledge of security and compliance requirements for fielded autonomous systems

Responsibilities

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 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
Support internal R&D investments in robotic autonomy and physical AI systems
Author and contribute to proposals, journal publications, technical reports, and patent disclosures
Sustain and grow sponsor-funded programs
Mentor junior engineers in mechanical design, robotics integration, and autonomy development