Rl Controls Jobs (NOW HIRING)

The Role

Self-Traversal is the locomotion problem at the center of GRAM: moving across arbitrary 3D structure, in any body orientation, with no assumption that the next contact patch is flat, known, or floor-like.

You will own the locomotion stack that makes this real on hardware. The near-term benchmark is simple to state and hard to achieve: a multi-legged robot should cover the usable structure of a complex steel lattice structure from a single placement, using learned policies, local contact intelligence, and perception-conditioned foothold selection.

The technical shape is specific: redundant contact on a multi-legged platform; learned contact schedules that generalize across substrate geometry; vision-conditioned local foothold selection from raw geometry; and gravity-agnostic stability across vertical, lateral, and inverted orientations.

This is not a pure simulation role. You will train policies, deploy them on physical robots, break them against real contact mechanics, and close the loop between simulator, controller, perception, adhesion, and hardware.

What You Will Do

• Own GRAM's Self-Traversal locomotion policy from simulation through hardware deployment.
• Build contact-aware RL environments and curricula for arbitrary 3D structure, with domain randomization across geometry, contact mechanics, adhesion, and gravity/body orientation.
• Develop vision-conditioned foothold and path-selection systems that use raw geometry and local perception rather than flat-ground or height-map assumptions.
• Work with mechatronics, firmware, and adhesion teams so the controller exploits the actual foot, gripper, microspine, magnetic, or compliant contact mechanism.
• Create evaluation loops for sim-to-real transfer, coverage, recovery, failure classification, graceful degradation under actuator/sensor/contact failures, and hardware regressions.
• Extend locomotion toward multi-robot traversal, where several robots occupy one structure and coordinate coverage without centralized micromanagement.

What We Are Looking For

• You have built or materially contributed to a robot locomotion stack on real hardware.
• You have personally taken a learned policy, controller, or planning stack from simulation into physical deployment.
• You have worked with multi-legged or contact-rich platforms: hexapods, RHex-like systems, quadrupeds, climbing robots, inspection robots, or hardware that must reason through redundant contacts.
• You are fluent in Python and comfortable in at least one modern robotics stack: Isaac Lab, legged_gym, rsl_rl, MuJoCo, MuJoCo MPC, Drake, Pinocchio, OCS2, Crocoddyl, ROS2, or an equivalent internal stack.
• You understand both modern reinforcement learning and classical contact mechanics. You do not need to be doctrinaire about either; we care about what survives contact with hardware.
• You can debug across abstraction layers: policy behavior, contact model, perception artifact, actuator limit, firmware timing, adhesion failure, and mechanical failure.

Strong Signals

• Publications, open-source work, or deployed systems in legged locomotion, learned control, contact-rich robotics, climbing robotics, or sim-to-real transfer, especially around RSS, CoRL, ICRA, IROS, NeurIPS, ICML, or ICLR.
• Experience with vision-conditioned locomotion, foothold selection, or perception-in-the-loop control using RGB, depth, event cameras, tactile sensing, or local geometry.
• Work on non-planar contact: climbing, inversion, microspines, gecko-style adhesion, magnetic adhesion, compliant feet/grippers, asteroid mobility, or any system where the gravity vector relative to the body is not fixed.
• Depth in sim-to-real policy generalization across substrate geometries, not only policy performance on a fixed benchmark environment.
• Familiarity with whole-body contact-rich analytical control such as TSID, Pinocchio, OCS2, or Crocoddyl, including hybrid stacks where analytical contact-force regulation sits under a learned policy.
• Relevant lab or project lineage such as ETH RSL, MIT Improbable AI, Berkeley Hybrid Robotics, Stanford IPRL, NVIDIA Isaac, Oxford ORI, CMU LeCAR/Biorobotics, UCSD Wang Lab, JPL LEMUR/Parness, Stanford BDML/Cutkosky, Penn Kodlab, KAIST CLS, or comparable industrial robotics teams.
• Field deployment scars: you have watched the robot fail somewhere inconvenient and made it better.

Not A Fit

• Pure simulation-only reinforcement learning with no hardware deployment.
• Pure perception or SLAM work with no control responsibility.
• Pure MPC or trajectory optimization with no fluency in learned policies.
• A preference for clean benchmark worlds over messy physical systems.

Compensation

Base salary range for this role: $150,000-$250,000 USD. Actual compensation will depend on experience, demonstrated technical depth, and level of ownership.

This role also includes significant early-stage equity, health/dental/vision coverage, paid meals, and relocation assistance.