Senior / Staff Sim-to-Real Engineer

JOB SUMMARY

We are looking for an experienced and autonomous Senior/Staff Sim-to-Real Engineer to bridge the gap between simulation and physical hardware for our advanced tendon-driven robotic hands.

Simulating tendon-driven mechanisms is notoriously difficult due to complex routing, friction, elasticity, and non-linear transmission dynamics. In this role, you will be responsible for creating elegant, highly accurate abstractions of these complex mechanisms for our simulators, and proving they work through rigorous empirical testing and system identification.

You will play a critical role in our control pipeline, ensuring that Reinforcement Learning (RL) policies trained in simulation successfully transfer to physical hardware. As a senior member of the team, you will have the autonomy to define our system identification roadmap, establish testing protocols, and drive the sim-to-real engineering culture.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Tendon Mechanism Abstraction: Develop and implement simple, computationally efficient, and highly accurate physical abstractions for complex tendon-driven mechanisms in simulation.
• System Identification & Validation: Design and execute empirical testing protocols to identify system parameters and prove that your simulation abstractions accurately reflect physical reality.
• Actuator Characterization: Perform rigorous system ID and control tuning on low friction robotic actuators to ensure high-fidelity torque and effort mapping.
• Rigid Multi-Body Calibration: Drive the kinematic calibration, inertial parameter identification, and effort production verification across our multi-body hand systems.
• Sim-to-Real Pipeline Ownership: Collaborate closely with the RL and Controls teams to ensure the dynamics engine and domain randomization strategies perfectly support zero-shot or few-shot transfer of control policies onto physical hardware.
• Technical Leadership: Operate autonomously to identify bottlenecks in the sim-to-real pipeline, propose solutions, and lead the technical execution from concept to deployed hardware.

SKILLS AND REQUIREMENTS

• Experience Level: 5+ years of industry or development experience post graduation in robotics, controls, or physical simulation, operating at a Senior or Staff level.
• Sim-to-Real Expertise: Proven track record of successfully transferring RL-based control policies from simulation to physical hardware. (Experience in locomotion or whole-body control is highly valued and directly applicable).
• System ID & QDD Actuators: Deep, hands-on experience with control, system identification, and characterization of Quasi-Direct Drive (QDD) actuators.
• Dynamics & Calibration: Strong foundation in rigid multi-body dynamics, kinematic calibration, and inertial identification.
• Hardware Profiling: Proven experience verifying effort production, characterizing friction/backlash, and diagnosing hardware-software discrepancies in complex mechanisms.
• Autonomy: Demonstrated ability to take an open-ended problem (e.g., "our policies fail when the tendon stretches"), design an empirical test, update the simulation model, and resolve the reality gap independently.

EDUCATION and/or EXPERIENCE

Senior Engineer:

• BS, MS or PhD in Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or a related field.
• 5+ years of relevant industry experience in robotics, controls or physical simulation with a BS (or 3+ years with an MS, or 1-2 years with a PhD)

Staff Engineer:

• MS or PhD in Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or a related field.
• 10+ years of relevant industry experience in robotics, controls or physical simulation with a BS (or 8+ years with an MS, or 6+ years with a PhD).

Preferred Qualifications

• Specific experience modeling, calibrating, or controlling tendon-driven or cable-driven robotic systems.
• Familiarity with modern RL environments and physics simulators (e.g., MuJoCo, Isaac Sim/Gym, Drake, or Bullet).
• Experience with sim-to-real techniques such as Domain Randomization, System ID-based digital twins, or Actuator Net modeling.

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate