Senior Member of Technical Staff Reinforcement Learning for Policy Post-Training

Position Summary:

Youll advance reinforcement learning for manipulation alongside a world-class teamusing RL to push our robot policies on contact-rich dexterous tasks beyond what imitation and supervised methods reach. Working hand in hand with the manipulation controls and simulation teams youll own the reward curriculum and algorithm design at the core of that work and help carry it through to results that hold up on real hardware doing real manipulation.

Core Responsibilities:

• RL for Manipulation: Design and run RL for contact-rich manipulation including reward design on/off-policy methods and stable training at scale.
• Sim-to-Real: Drive the RL side of sim-to-real for manipulationdomain randomization system identification and the iteration that makes policies transferworking closely with the simulation team on the shared transfer loop.
• Closing the Loop: Combine simulation offline data and real-world feedback into manipulation policies that improve predictably rather than by luck.
• Reward & Task Design: Craft rewards curricula and task setups that make hard manipulation skills learnable without reward hacking.
• Collaboration & Mentorship: Partner with the manipulation controls and simulation teams and mentor MTS engineers on the RL stack.

Required Qualifications:

• RL Depth: Strong hands-on RL experience with demonstrated results on hard real-world or large-scale problems.
• Manipulation: Experience applying learning to manipulation or contact-rich control (grasping dexterous or bimanual manipulation).
• Sim-to-Real: Demonstrated success transferring learned policies from simulation to physical robots.
• Ownership: Ability to design rigorous experiments and drive an ambiguous workstream independently to a result.
• Software Engineering: Strong fundamentals and comfort with large training/simulation codebases.

Preferred Qualifications:

• Experience with dexterous hands high-DOF manipulators or bimanual systems.
• Familiarity with GPU-accelerated simulation for manipulation (Isaac MuJoCo etc.).
• Background in offline RL learning from demonstration or combining RL with imitation.

Walden Robotics offers a competitive total compensation program including salary annual cash bonus company equity company-subsidized insurance programs 401(k) with company match flexible PTO daily lunch and other benefits. The pay ranges noted on our posts are for salary only.

Walden Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age ancestry color family or medical care leave gender identity or expression genetic information marital status medical condition national origin physical or mental disability political affiliation protected veteran status race religion sex (including pregnancy) sexual orientation or any other characteristic protected by applicable laws regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process please send a request to .

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