Tech Stack

Description

You will lead research in embodied AI for autonomous defense systems, covering reinforcement learning, vision-language-action modeling, sim-to-real transfer, and multi-agent coordination. Your work will translate foundational research into scalable, robust models for real-world deployment.

Requirements

• PhD in Computer Science, Robotics, Machine Learning or related field (or equivalent industry experience)
• Strong publication record in top-tier venues
• Deep expertise in RL, 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

Responsibilities

• Conduct original research in embodied AI including learning from demonstration, RL, memory, reward, vision-language modeling, and world modeling
• Build, test, and benchmark large-scale models for perception, decision-making, and control in simulation and physical environments
• Investigate transfer learning and continual learning across diverse robotic domains
• Collaborate with engineering, robotics, and field teams to integrate research into operational systems
• Support field tests and data collection campaigns to evaluate system performance in realistic environments