Job Opportunities API

The Mission GRAM is a self-replication company creating machine labor for the physical economy. Our first research frontier is self-preservation: 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. 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,