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

Position Summary:

You'll advance reinforcement learning for manipulation alongside a world-class team—using 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, you'll 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 manipulation—domain randomization, system identification, and the iteration that makes policies transfer—working 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 [email protected].

Walden Robotics participates in E-Verify. If you receive an offer of employment from Walden, you will need to go through the E-Verify process of digital verification of your employment authorization documents as provided on the Form I-9. Participation in E-Verify does not limit your right to work and verification will only be completed after you become an employee with Walden Robotics.