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# Technical Program Manager, Robotics Data

- **Company:** The Robotics Foundry and Digital Greenhouse, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Computer Vision, Data Governance, Python (Programming Language), Operational Databases, Program Design Languages, SQL Databases, Management of Software Versions, Data Processing, Data Pipelines
- **Published:** May 25, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b24b8c395e1b9427

## About the Role

Do you have experience in Program design?, * 3-5 years as a strong PM or TPM with exposure to hardware, data pipelines, or physical workflows

 * Proven ability to build data collection or technical operations programs from the ground up and improve on existing ones
 * Comfortable owning task specs, QA programs, and standardization
 * Equally effective at a desk and on the floor, with deep hands-on experience calibrating a rig one hour and writing a stakeholder update the next
 * SQL or Python proficient enough to pull your own data and build your own dashboards
 * Track record tracking and improving program metrics (usable hours, mix accuracy, SLA attainment, rejection rates)
 * Experience providing technical input in a cross-functional environment with researchers and engineers
 * Collaborative by nature, you consider downstream impacts on operators, model teams, and infrastructure when making program decisions
 * Bias toward action, scrappy problem solving, and moving fast without sacrificing rigor

Nice to Have (Not Required)

 * Experience supporting vendor or hardware supplier relationships
 * Exposure to robotics, VLA models, or foundation model training data
 * Experience scaling a data collection program from prototype to high-volume production
 * Familiarity with computer vision, sensor calibration, or robotics data pipelines

## Description

This role is for a Technical Program Manager who thrives on turning model team requirements into production data, equally comfortable architecting QA systems from first principles and calibrating a rig on the floor. You will define what gets captured, how it lands, and whether it is usable, from early task spec through pre-delivery validation. You will stand up the data collection program from scratch and drive rapid build-test-learn cycles to deliver a dataset that is both rigorous and scalable.

You will be the day-to-day interface with our model team and the frontier labs we deliver to, and the source of truth for hours collected, hours delivered, and hours usable. You will partner closely with the Operations Manager, Robotics Data, who owns the workforce, floor execution, and daily fleet readiness., * Manage the rig fleet end-to-end (UMI grippers, GoPros, VR headsets and controllers), including procurement, lifecycle, asset tracking, spares, and vendor relationships

 * Own rig calibration cadence and drift detection, including GoPro intrinsics, fisheye calibration, UMI gripper width drift, and per-operator VR eye-tracking calibration
 * Define success criteria with the model team, translating high-level data needs into rejection rules an assembler can apply consistently
 * Author SOPs and hand them to the Operations Manager for floor execution
 * Own new task ramp-up, including spec, SOP, pilot batch design, and quality gate sign-off before full production
 * Curate gold-standard demos (20-50 reference demos per task), maintained against drift
 * Run post-capture QA (sampling + success classifier) and pre-delivery validation against model team spec
 * Track diversity and coverage across task variants, object positions, lighting conditions, and operator styles
 * Run daily capture logs and weekly dataset reporting to model and research stakeholders, including hours captured, hours usable, mix vs. target, top rejection reasons, and blockers
 * Own task spec versioning, coordinating SOP updates, retraining triggers, and data v1/v2 marking when the model team changes a spec
 * Own the 48-hour handoff SLA, including staging, upload integrity, episode schema enforcement, and metadata completeness (operator ID, task ID, success label, timestamp written atomically)
 * Run RCA when batches get rejected and close the loop with the floor
 * Define and report against program-level OKRs for usable hours delivered, mix accuracy, and SLA attainment
 * Own data governance and compliance, including episode-level metadata for auditability, retention and deletion policies, and adherence to data handling standards required by frontier lab partners


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