Senior Robotics Engineer, Data Collection

Gram Games; San Francisco (CA); On-site; USD 170,000 - 220,000; Full time

Benefits offered by this job

Health coverage

Equity

Relocation bonus

On-site role

Job summary

Gram Games is seeking a senior engineer to own the systems that generate high-quality robot data from physical work, spanning demonstration, teleoperation, intervention, autonomous operation, and data capture. You will define capture contracts to ensure evidence is reusable for training and evaluation.

Based on Palo Alto on-site, you will design campaigns, instrument tools, and validate data quality, latency, and safety across sessions, with an emphasis on measurable improvements to model or

Qualifications

• 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.

Responsibilities

• 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.

Skills

C++/Python

Linux

Robotics data collection

Latency measurement

Data quality assessment

Education

Bachelor's degree in CS/EE/ME/Robotics or equivalent

Tools

Robot middleware

Sensor streams

Hardware debugging

Telemetry tools

Job description

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

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.

What you will do

• 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.

Minimum qualifications

• 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.

Preferred experience

• 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.

The annual base salary range for this Palo Alto position is $170,000–$220,000. 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.