Senior Robotics Engineer, Data Collection

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 called insectoids 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.

You will own the systems and methods that generate high-quality robot data from physical work, spanning demonstration, teleoperation, intervention, autonomous operation, instrumentation, operator tooling, scenario execution, and source-quality control. You will translate capability gaps into collection campaigns and use measured downstream results to decide what the machines should experience next.

This is a senior engineering role responsible for how experience is produced and validated at capture, including meaningful variation, failures, recoveries, and episode-level evidence. You also define the capture contracts that keep recorded experience usable for reproducible datasets and replay. Success means each campaign yields data that can change a training or evaluation decision—not operating hours without learning value.

• →Build field-capable demonstration interfaces and robust systems for teleoperation, intervention, autonomous rollout, and recovery-data collection in realistic physical work.
• →Translate model failures and evaluation gaps into controlled scenarios, collection protocols, sampling priorities, and measurable acceptance criteria.
• →Design operator interfaces with explicit command authority, latency budgets, feedback, safe handoff, and emergency behavior.
• →Instrument demonstration tools, robots, operators, and environments so perception, action, timing, contact, intervention, and task outcome remain aligned and valid at capture.
• →Commission collection stations and diagnose failures spanning sensors, controls, networking, operator input, robot execution, and recorded data.
• →Define operator procedures, calibration checks, training, and escalation paths that produce consistent evidence across people and sessions.
• →Measure whether collected experience changes model or system performance, then use the result to refine the next campaign.

• Bachelor's degree in computer science, electrical engineering, mechanical engineering, robotics, or a related field, or equivalent practical experience.
• Strong C++ or Python programming ability in Linux, including experience with robot middleware, sensor streams, command interfaces, and hardware debugging.
• Direct experience building and repeatedly operating a teleoperation, shared-control, portable demonstration, instrumented task-tool, or robot-data collection system for physical work.
• Demonstrated ability to characterize end-to-end latency, timing, calibration, command safety, and data quality using instrumented tests.
• Experience converting a learning, test, or capability objective into a physical collection protocol whose data produced a measured change in model or system performance.

• Portable demonstration interfaces, instrumented task tools, VR, motion capture, haptics, retargeting, remote robot operation, or shared autonomy.
• Imitation learning, reinforcement learning, active learning, or failure-directed data collection.
• Networked real-time systems, video transport, time synchronization, or edge data capture.

This role is based on-site in San Francisco with continuous access to physical robots and collection systems.

After submitting your application, we review your portfolio and any exceptional work you've shipped. If your application demonstrates the caliber we seek, you'll enter our interview process, which is designed for speed and substance. We aim to complete it within one week from start to finish.

GRAM expects deep trust and ownership from its people, and we begin by extending the same to candidates. We treat your information, prior work, and conversations with discretion.