AI Researcher

The future of defense will be decided by those who field intelligent machines at scale. At Scout AI, we’re developing Fury, the first robotic foundation model for defense, to give U.S. forces overwhelming, adaptable, and autonomous power across every domain. Fury enables human operators to command fleets of robots through natural language, and empowers those machines to sense, decide, and act together as one. This mission will ask everything of us: urgency, precision, and relentless work.

The RoleWe’re hiring a Staff or Senior AI Researcher on the Fury Team to help push the frontiers of embodied intelligence for autonomous defense systems. You will lead research in core areas including reinforcement learning, vision-language-action modeling, sim-to-real transfer, dynamic AI memory, and multi-agent coordination. Your work will be foundational to the development, training, and deployment of Fury.

This role sits at the intersection of cutting-edge ML research, real-world robotics, and defense. You'll translate foundational research into scalable, robust models that operate in unstructured, contested environments, from concept to deployment. We’re a startup. You’ll be moving fast, context-switching daily, and helping define the culture and process as we go. This is a rare opportunity to come in early and architect the future of defense. 

Responsibilities

• Conduct original research in embodied AI, including learning from warfighter demonstration, reinforcement learning, memory, reward, vision-language modeling, and world modeling
• Build, test, and benchmark large-scale models for perception, decision-making, and control in simulated and physical environments
• 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

Qualifications

• PhD in Computer Science, Robotics, Machine Learning, or a related field plus 4+ years of applied research experience (industry, postdoc, or lab), or 8+ years of equivalent industry research experience without a PhD
• Strong publication record in top-tier venues
• 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