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.
You will build manipulation capabilities for insectoids operating across changing geometry, orientation, surface condition, and payload. The work spans task and motion planning, learned policies, contact reasoning, trajectory generation, force control, and deployment on physical hardware. You will determine where model-based, learned, foundation-model, and hybrid approaches perform best against measured results.
This is a staff-level individual-contributor role with authority over the manipulation architecture, cross-stack technical decisions, and measured manipulation-capability readiness. You will establish system boundaries and evaluation standards, lead design and failure reviews, mentor engineers, and remain hands-on in C++ and Python. Success is a manipulation system that completes defined physical tasks repeatedly, exposes why it failed, and improves against physical evidence rather than a scripted laboratory demonstration.
The annual base salary range for this San Francisco Bay Area position is $200,000–$260,000. Health coverage, benefits, and generous equity come with the role. This role is on-site in the San Francisco Bay Area. 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.
| Location | San Francisco, CA |
| Salary | $200,000–$260,000 Per Year |
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