About Us

Foundry Robotics is building an AI-native robotics manufacturing company focused on deploying advanced assembly and production capability for leading robotics companies and national-security-critical hardware. Basically, we’re building robots that build robots.

We are reimagining manufacturing through advanced robotics. Our mission is to rebuild the American manufacturing industry as an AI-first, assembly-focused, dual-use contract manufacturer. We aim to empower manufacturers with intelligent, efficient, and adaptable robotic systems that redefine productivity and quality.

The Role

This role is for a Strategic Project Lead 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.

Key Responsibilities

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

What We're Looking For

Founder mentality: We love people who build like it’s their own company. You move fast, take ownership, and find a way through when the path isn’t obvious. You have the drive to make big things happen and the judgment to take smart risks along the way. If you prefer a clearly defined lane, a predictable pace, and all the answers before you start, there’s nothing wrong with that—but Foundry might not be the right fit.

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