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. The pay ranges noted on our posts are for salary only.
The pay range for this role is:
180,250 - 240,250 USD per year (BOS)
