AI Engineer - Fury Team

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.

We're looking for an AI Engineer to join the Fury Orchestration Team with a deep passion for developing and deploying multimodal foundation models that power autonomous teams of robots. Your work will directly influence how our system reasons, plans, remembers and coordinates across multiple robots to accomplish long-horizon missions. Depending on your background and interests, you’ll contribute across the stack: model training and evaluation, reinforcement learning, evaluation and simulation, reasoning and memory architectures, multi-agent coordination, and edge inference optimization. Expect to rapidly prototype ideas against rigorous evaluations, test ideas on real robotic mission rollouts, and break new ground as we create the first robotic foundation model for defense.

We’re a startup. You’ll be moving fast, context-switching daily, and helping define the culture and process as we go. You’ll have substantial ownership, influence over technical direction, and the opportunity to help build the future of autonomous defense systems from the ground up.

• →Design, train, and evaluate multimodal foundation models that enable multi-robot mission execution
• →Drive model development decisions including model architectures, data mixtures, curriculum design, and training recipes informed by rigorous experimentation
• →Curate and generate large-scale training datasets from both synthetic and real-world data engines
• →Develop memory, communication, planning, and tool-use capabilities for AI agents operating across teams of robots
• →Be obsessed with evaluation: design benchmarks, metrics, and testing methodologies that enable rapid iteration across the team
• →Build and improve simulation environments targeted for synthetic data generation, evaluation, and reinforcement learning
• →Translate foundational research into deployable, real-time perception and decision-making systems
• →Collaborate across engineering, robotics, and mission teams to integrate AI systems with onboard autonomy stacks
• →Conduct field trials and mission operations to validate system performance e under real-world constraints

• 2+ years of hands-on experience building and deploying AI models, including 2+ years working with multimodal models or agentic AI systems 
• Proficiency in Python, PyTorch, and modern machine learning infrastructure
• Experience training, finetuning, and evaluating large-scale deep learning models using distributed compute infrastructure
• Demonstrated experience improving model performance through data curation, training strategy, evaluation design, and rigorous experimentation
• Solid grasp of modern model training techniques including SFT, DPO, RLVR
• Demonstrated ability to take research from prototype to product in fast-moving environments
• BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or related technical field, or equivalent practical experience. Advanced graduate research in AI, robotics, or machine learning is a plus.
• Bonus: Experience building closed-loop simulation, evaluation, or reinforcement learning environments for agentic or physical AI systems
• Must be a U.S. Person due to required access to U.S. export controlled information or facilitie