Key Responsibilities

• ​Develop robust simulation environments for dexterous manipulation, task prototyping, system
  validation, and sim-to-real evaluation.
• Build digital twins of robots, end-effectors, sensors, and workcells for software-in-the-loop and
  hardware-in-the-loop testing.
• Simulate vision, tactile, force/torque, and proprioceptive sensors with realistic noise, latency, and
  task-relevant physical behavior.
• Support the integration of learning-based methods such as RL, IL, and related approaches into
  simulation environments to help researchers prototype and evaluate ideas efficiently.
• Create scalable data collection pipelines for simulation and real-world teleoperation, including
  logging, dataset management, quality control, and supporting interfaces.
• Implement teleoperation interfaces and policy-in-the-loop evaluation tools.
• Evaluate simulation fidelity and trained system performance using synthetic and real-world data
  collection and manipulation benchmarks.
• Oversee large-scale data collection procedures and help ensure consistency, usability, and quality
  of collected data.
• Collaborate with cross-functional robotics, controls, perception, hardware, and research teams
  across multiple projects.
• Document results and contribute to internal reports, demos, technical presentations, and
  intellectual property.

Minimum Qualifications

• MS or PhD in Robotics, Computer Science, Electrical or Mechanical Engineering, or related field.
• Proficiency in C++ and Python for robotics software development.
• Experience with ROS and robot control stacks.
• Experience building simulation environments, digital twins, or validation infrastructure for robotic
  manipulation.
• Experience using modern simulators such as NVIDIA Isaac Sim, MuJoCo, or similar platforms.
• Experience with data collection pipelines in simulation and/or real robotic systems.
• Familiarity with implementation of learning-based methods sufficient to support rapid prototyping
  and integration of research ideas into practical simulation workflows.
• Strong foundation in robot kinematics, dynamics, and control.

Bonus Qualifications

• Experience with dexterous or multi-finger robotic hands.
• Whole-hand control, grasp synergies, or in-hand manipulation experience.
• Tactile sensing and tactile simulation experience.
• Experience with teleoperation systems, operator interfaces, or demonstration collection.
• GPU acceleration or large-scale simulation and data collection pipelines.
• Experience defining evaluation metrics and analyzing manipulation performance and sim-to-real gaps.
• Experience developing and implementing model-based and learning-based manipulation systems.
• Publications, patents, or open-source contributions in robotics or manipulation.