Staff Applied Scientist - AI & Robotics

Overview
Join a cutting-edge AI Research team developing end-to-end robot learning systems for dexterous manipulation and autonomous task execution in real-world environments. In this role, you will architect and deploy core components of embodied AI systems spanning perception, policy learning, simulation, and physical robotics. You will work closely with robotics, AI infrastructure, and hardware teams to build scalable, production-ready solutions.

Key Responsibilities

• Design and implement advanced robot learning architectures (e.g., diffusion policies, ACT, VLM/VLA-guided agents, imitation learning) for manipulation, planning, and autonomous sequencing.
• Build end-to-end training pipelines integrating multimodal inputs (RGB, depth, proprioception, force/torque, LiDAR, tactile).
• Develop policy inference and closed-loop control solutions linking perception, planning, and real-world execution.
• Apply large-scale AI models LLMs, VLM/VLAs, diffusion models to embodied tasks, grounding, and sim-to-real transfer.
• Collaborate with cross-functional teams to deploy robust and safe robot policies on hardware platforms.
• Lead data strategy across demonstrations, teleoperation, simulation pipelines, and evaluation frameworks.

• PhD in a relevant STEM field, or Master s with equivalent industry experience in robotics, robot learning, or embodied AI.
• Minimum Exp. Required - 8yrs - 20yrs
• Hands-on experience training, evaluating, and deploying ML models on robotic systems (real-world or simulated).
• Strong understanding of modern AI architectures (Transformers, diffusion models, VLM/VLAs, CNNs).
• Excellent PyTorch skills, including custom module development, debugging, and performance optimization.
• Practical experience with ROS/ROS2 and integrating learned policies into manipulation or control workflows.
• Demonstrated contributions via publications, open-source work, or deployed robotics systems.

• Experience with dexterous manipulation, multi-step task policies, or autonomous robotic behaviors.
• Expertise in robotics perception (3D understanding, force/tactile sensing, multimodal fusion, affordance modeling).
• Familiarity with simulators such as Isaac Sim, Mujoco, Gazebo, or PyBullet, and experience with hardware transfer.
• Experience adapting foundation models for embodied control tasks.
• Track record of production-ready robotics deployments or reproducible research artifacts.