Developing intelligent machines at scale for defense systems
Requirements
- Deep expertise in one or more of the following: reinforcement learning, imitation learning, vision-language models, sim-to-real, world modeling, or agentic AI
- Hands-on experience building and evaluating models for embodied agents or autonomous systems
- Proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX
- Experience working with large-scale datasets and high-dimensional sensor inputs
- Experience with multi-agent systems or large-scale robotic coordination (bonus)
- Familiarity with ROS, real-time control loops, or embedded inference (bonus)
- Experience integrating ML models with physical robotic platforms (bonus)
Responsibilities
- Conduct original research in embodied AI
- Build, test, and benchmark large-scale models for perception, decision-making, and control
- Investigate transfer learning and continual learning paradigms across diverse robotic domains
- Collaborate with engineering, robotics, and field teams to integrate research into operational systems
- Lead research strategy and roadmap across key areas of autonomy and machine learning
- Support field tests and data collection campaigns to evaluate system performance in realistic environments
Other
- PhD in Computer Science, Robotics, Machine Learning, or a related field (or equivalent industry research experience)
- Must be a U.S. Person due to required access to U.S. export controlled information or facilities
- Background in DoD, aerospace, or other mission-critical deployments (bonus)
- Relocation assistance (depending on role eligibility)
