# Senior Robotics Engineer, Data Collection at Gram Games

> United States · Senior · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Agram%3A4377780009 (read 2026-11-07).

## Facts (observed)

- Company: [Gram Games](https://alderhq.app/companies/gram-games)
- Location text: San Francisco Bay Area, California, United States; Palo Alto
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco Bay Area, California, United States; Palo Alto")
- Posted: 2026-08-21 (78 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/gram/jobs/4377780009
- Skills listed (inferred): Python, C++

## Pay and sponsorship

- Posted pay: $170k–$220k per year (from the description)
- Market band for similar posted roles (SF Bay Area, n 1042): p25 $182k, median $205k, p75 $229k
- Licensed UK sponsor: registered as "Gram Games Limited" (checked 2026-11-03, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers))

## Description (the employer's text)

### 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 San Francisco Bay Area position is $170,000–$220,000. 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.

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Agram%3A4377780009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.