Gram Games

Gram Games

Controls Engineer, Locomotion

El Segundo, California, United States

Sponsorship not specified$150k-$250kDetected 70 days ago
PythonRoboticsResearch

About 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 train policies, deploy them on physical robots, break them against real contact mechanics, and close the loop between simulator, controller, perception, adhesion, and hardware.

Responsibilities

  • 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.
  • If your application demonstrates the caliber we seek, you'll enter our interview process, which is designed for speed and substance.

Requirements

  • 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 can debug across abstraction layers: policy behavior, contact model, perception artifact, actuator limit, firmware timing, adhesion failure, and mechanical failure.

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.

Benefits

  • Experience with vision-conditioned locomotion, foothold selection, or perception-in-the-loop control using RGB, depth, event cameras, tactile sensing, or local geometry.
  • Develop vision-conditioned foothold and path-selection systems that use raw geometry and local perception rather than flat-ground or height-map assumptions.
  • 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.
  • You understand both modern reinforcement learning and classical contact mechanics.
  • This role also includes significant early-stage equity, health/dental/vision coverage, paid meals, and relocation assistance.

Company info

  • GRAM is a self-replication company creating machine labor for the physical economy.
  • the base case of physical self-replication.
  • We are building a new class of machines that can survive, coordinate, and recover without humans.
  • We believe scalable machine labor requires more than single-agent task generality or machines shaped in our image.
  • Our work spans hardware, controls, reinforcement learning, multi-agent coordination, materials science, evaluation, and world models.
  • Join us to solve closure and multi-agent environment generality in industrial domains where demand for labor is effectively unbounded.
  • It is our mission to make humanity galactic.
  • What We Are Looking For
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This listing is sourced directly from Gram Games's careers page and normalized into a canonical job model.