Senior Software Engineer, Controls
AI Summary
A Software Controls Engineer, Locomotion will own the locomotion stack for a six-legged insectoid robot that climbs steel infrastructure using magnetic end effectors. The role involves building perception-conditioned path planning, foothold selection, and gravity-agnostic stability for vertical, lateral, and inverted traversal on complex lattice structures.
About this role
The Mission
GRAM is a self replication company creating populations of insectoids for the physical economy.
Our first research frontier is self-preservation: the base case of physical self-replication. Our machines will survive, coordinate, and recover without humans. We believe scalable machine labor requires more than single-agent task generality or machines shaped in our image.
About the role
We are looking for a Software Controls Engineer, Locomotion with 5-7+ years of experience to own the locomotion stack for a first-of-its-kind six-legged Insectoid robot designed to climb and traverse complex steel infrastructure. You'll join a small, scrappy founding team building something that doesn't exist anywhere else. It is a hexapod that uses magnetic end effectors to climb high-voltage towers, bridges, and pipelines for inspection and manipulation. This is a ground-floor opportunity where your contributions will directly shape the direction of the company. We need someone with real hardware deployment experience, a MacGyver mindset, and the grit to iterate rapidly in a test-build-break loop every single day.
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.
What you will do
- Getting the robot walking reliably and integrating magnetic adhesion for vertical/near-vertical traversal on steel lattice structures
- Building perception-conditioned path planning and foothold selection so the robot can navigate dozens of different lattice structure geometries
- Reviewing and rebuilding existing control software with proper software development practices, discarding what doesn't work and establishing solid foundations
Minimum qualifications
- Bachelor's degree in robotics, mechanical engineering, electrical engineering, computer science, or a related field, or equivalent practical experience.
- Demonstrated ownership of architecture and technical direction for a deployed manipulation system spanning planning, sensing, control, monitoring, and recovery.
- Strong C++ and Python skills, including experience with a robotics framework such as ROS 2, Drake, MoveIt, Pinocchio, or an equivalent internal stack.
- Built and deployed a closed-loop manipulation capability on a physical robot; you can explain its architecture and provide measured results from repeated trials.
- Led a failure-driven redesign that separated planning, perception, control, and hardware causes and produced a correction that survived repeated physical test.
Preferred experience
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Python
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Isaac Lab
-
legged_gym
-
rsl_rl
-
MuJoCo
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MuJoCo MPC
-
Drake
-
Pinocchio
-
OCS2
-
Crocoddyl
-
ROS2
-
Reinforcement Learning
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Sim-to-Real Transfer
The annual base salary range for this Palo Alto position is $150,000–$250,000. Health coverage, benefits, and generous equity come with the role. This role is on-site in Palo Alto. A relocation bonus is available. We hire to start as soon as possible. We review what you’ve shipped; if it’s the caliber we seek, interviews take about a week, start to finish. We treat your work and conversations with discretion.
Skills
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