Technical Lead - AI Manipulation and Simulation (human)

• Multimodal Learning Architecture: You design and build systems that fuse camera, depth, tactile, IMU, language, and robot-state inputs into executable robot actions. You define how these modalities combine and translate into policies that run on our dexterous hands.
• Simulation Infrastructure & Data Pipelines: You build and maintain GPU-accelerated simulation environments and the data pipelines that make policy training scalable.
• Sim-to-Real Transfer: You lead the engineering work that closes the gap between simulation and physical hardware.
• Team Leadership & Engineering Direction: You translate research problems into concrete milestones, guide engineers, and work across ML, robotics, and hardware teams to ship learning systems that run on physical robots.

• 6 or more years of experience in computer science or a related engineering field, with meaningful time delivering AI systems on physical robotic hardware
• Hands-on experience leading or co-leading the design of multimodal manipulation systems combining vision, language, tactile, and proprioceptive inputs
• Proven track record building simulation infrastructure (Isaac Lab and Isaac Sim, or MuJoCo) for reinforcement learning and sim-to-real transfer
• Deep practical knowledge of imitation learning (including diffusion policies), deep RL, and hybrid learning approaches on real robot hardware
• Experience with data pipelines for heterogeneous, high-frequency sensor data: teleoperation, tactile, vision, depth, and robot state
• Strong Python and C++, with experience in ROS2 and embedded or real-time systems
• Ability to translate ambiguous research problems into concrete engineering milestones and to grow junior engineers

Nice to have:

• Experience with high-DOF, tendon-driven, or tactile-heavy dexterous hands
• Familiarity with VLA or vision-language-action architectures and large-scale pre-training workflows
• Contributions to the robotics or AI research community (ICRA, IROS, CoRL, NeurIPS)