Senior Member of Technical Staff: Reinforcement Learning for Wholebody Control

Walden Robotics; Cambridge (MA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Salary

Annual cash bonus

Company equity

Insurance programs

401(k) with company match

Flexible PTO

Daily lunch

Other benefits

Job summary

Walden Robotics in Cambridge, MA seeks an experienced robotics engineer to develop learned control policies for humanoid robots. You will train in simulation, blend with model-based control, and own the sim-to-real pipeline to deploy reliable policies on real hardware.

The role emphasizes end-to-end deployment and collaboration across controls, hardware, and AI teams. This position requires strong RL for continuous control, proficiency in Python and C++, and experience with whole-body control on

Qualifications

• RL policies for continuous control using modern RL algorithms.
• Proven sim-to-real transfer to physical robots and deployment on real hardware.
• Solid grounding in control theory and robot dynamics.
• Experience with large-scale GPU simulation for policy training.
• Strong Python and C++ programming for training and deployment infra.
• Experience with whole-body control on legged/humanoid robots.
• Track record of taking policies from concept through training to field deployment.

Responsibilities

• Design, train, and refine reinforcement learning policies for whole-body control.
• Integrate learned policies with classical/model-based control methods for robustness.
• Own the sim-to-real transfer pipeline including domain randomization and system identification.
• Build and maintain infrastructure to train, evaluate, version, and deploy policies at scale.
• Coordinate with controls, hardware, simulation, and AI engineers to integrate policies.
• Evaluate policies rigorously in simulation and on hardware, ensuring safety and reliability.
• Collaborate across teams to fit policies into the broader robot stack.

Skills

RL for continuous control

Sim-to-real robotics

Control theory basics

GPU-scale simulation

Python & C++

Humanoid/legged robotics

Ownership of ML projects

Tools

GPU-accelerated simulation

Model-based control

Job description

Position Summary

You will develop the learned control policies that give our humanoid robots robust, capable whole-body motion-and get them running on real hardware in real deployments. This role sits at the intersection of reinforcement learning and classical control: you'll train policies in large-scale simulation, blend them with model-based control where that's the right tool, and own the sim-to-real pipeline that turns a promising policy into dependable behavior on a physical robot. This is not a research-only role. Your success is measured by robots that stand, balance, and manipulate reliably in the field, and by a training-and-deployment pipeline that lets the team ship improved policies again and again.

Core Responsibilities

• Policy Development: Design, train, and tune reinforcement learning policies for whole-body control - balance, locomotion, and coordinated manipulation - on a humanoid platform.
• RL + Control Integration: Combine learned policies with classical and model-based control techniques, choosing the right blend for robustness, safety, and performance. Interface the control stack smoothly with high-level motion commands.
• Sim-to-Real Transfer: Own the pipeline that transfers policies from simulation to hardware - domain randomization, system identification, and the iterative loop of closing the sim-to-real gap on real robots.
• Production Training Pipeline: Build and maintain the infrastructure to train, evaluate, version, and deploy policies repeatably, so improvements reach the fleet reliably and safely.
• Real-Robot Deployment: Bring policies up on physical robots, debug behavior in the real world, and harden them against the variability of real environments.
• Evaluation & Safety: Develop rigorous evaluation - in sim and on hardware - and ensure learned controllers behave safely and degrade gracefully at their limits.
• Cross-Functional Collaboration: Partner with controls, hardware, simulation, and AI engineers to define interfaces, improve models, and integrate policies into the broader robot stack.

Required Qualifications

• Reinforcement Learning Depth: Proven experience developing and training RL policies for continuous control, with strong command of modern RL algorithms and their practical failure modes.
• Real-Robot Sim-to-Real: Demonstrated success transferring learned policies from simulation to physical robots and deploying them on real hardware.
• Control Fundamentals: Solid grounding in control theory and robot dynamics, and the judgment to combine learned and model-based approaches.
• Large-Scale Simulation: Hands-on experience training policies in GPU-accelerated simulation at scale.
• Software Engineering: Strong Python and working C++ skills, with the ability to build training and deployment infrastructure that others can rely on.
• Humanoid or Legged Systems: Experience with whole-body control, locomotion, or manipulation on legged, humanoid, or similarly high-DOF robots.
• Ownership: Track record of taking policies from concept through training, transfer, and field deployment.

Preferred Qualifications

• Experience with model predictive control, whole-body QP controllers, or trajectory optimization.
• Background in system identification, actuator modeling, or contact-rich dynamics.
• Experience building ML training infrastructure and experiment-tracking workflows at scale.
• Familiarity with imitation learning, teleoperation data, or learning from demonstration.
• Publications or demonstrated results in legged locomotion or whole-body control.

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.

• salary
• annual cash bonus
• company equity
• company-subsidized insurance programs
• 401(k) with company match
• flexible PTO
• daily lunch
• 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 [e-mail].

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

At Walden Robotics, we envision a world where general-purpose robots dramatically improve the quality of life for all people- supporting us at home, at work, in factories, on farms, and beyond. To accomplish this, we are building a team of exceptional professionals who combine world-class technical skills with creative vision, grounded in humility and collaboration.