Most TPM roles focus on tracking timelines, coordinating milestones, and driving execution against an established plan. This role is different. You’ll own the operational backbone of a vision AI data program, ensuring ideas move seamlessly from hypothesis to data capture to validation to a governed dataset to a commercial product.
You’ll sit at the center of a complex ecosystem spanning a lean founding engineering team, capture hardware and annotation vendors, frontier AI and robotics companies, academic partners, and internal Legal, Site Operations, and Finance teams. Your mandate is to create operational continuity across these interfaces, remove friction, accelerate decisions, and maintain a disciplined path from experimentation to validated, production ready outcomes.
Stord Labs is an innovation center designed to evaluate emerging logistics technologies and help redefine the future of fulfillment. Within a live micro fulfillment environment, you’ll orchestrate the full lifecycle of technical experiments, ensuring promising concepts are rigorously tested, clearly validated, and efficiently transitioned into production when they demonstrate measurable business value.
This role is built for a systems oriented program leader who has operated in computer vision, ML, AI, robotics, or data intensive environments and can translate fluently between research, engineering, and warehouse operations without sacrificing speed or precision. You’ll be equally comfortable working with engineers and researchers, coordinating external partners, navigating operational constraints, and communicating outcomes to executive stakeholders.
The goal isn’t simply to manage the program. It’s to build the operating system that allows a new vision AI capability to move from idea to validated data product at scale.
What You'll Do
Own the evaluation lifecycle: Govern the end to end pipeline of experiments, ensuring every initiative starts with a clear hypothesis, measurable success criteria, and structured evaluation framework. Drive disciplined decisions to kill, iterate, or scale based on evidence.
Run hardware and vendor evaluations: Lead structured, evidence based evaluations across capture devices, depth sensors, gloves, edge compute, and data and annotation vendors. Own RFPs, scorecards, evaluation frameworks, and vendor selection.
Orchestrate the cross functional ecosystem: Serve as the primary integration point between the engineering pod and Legal, Site Operations, Finance, and warehouse operations. Coordinate requirements around consent, associate participation, scheduling, floor logistics, cost modeling, and operational readiness.
Manage the data pipeline program: Drive the path from pilot capture through annotation, validation, and secure buyer delivery. Own annotation throughput and QA cadence while establishing and tracking data quality, capture yield, and delivery metrics.
Manage partners and buyers: Coordinate technical and operational engagement with frontier AI and robotics companies and academic partners such as Georgia Tech. Translate customer requirements into clear program priorities and ensure datasets meet the standards required for validation and delivery.
Keep governance and risk on track: Ensure consent, privacy, and data rights requirements are addressed with Legal before and throughout capture activities. Identify and retire program risks while keeping executive sponsors informed of progress, tradeoffs, and decisions.
Basic Qualifications
Bachelor’s degree in Computer Science, Industrial Engineering, Electrical Engineering, Supply Chain, or a related technical discipline.
8+ years of experience in Technical Program Management, Engineering Management, or complex cross functional delivery roles.
Demonstrated experience running computer vision, ML, or data pipeline programs end to end, including data collection, annotation, model training pipelines, or data platform delivery.
Proven experience managing both hardware and data or annotation vendors, including RFPs, structured evaluations, scorecards, and strategic partnerships.
Comfortable operating in deeply technical CV, ML, and data environments and translating between research, engineering, and operations without losing precision.
Strong execution discipline and experience operating in agile, sprint based, or similar environments.
Demonstrated judgment in making and driving kill, iterate, or scale decisions based on data and outcomes.
High ownership and comfort operating in ambiguous, 0 to 1 environments.
Bonus Points
Experience in innovation labs, R&D environments, or advanced automation programs.
Familiarity with egocentric or robotics datasets, annotation platforms such as Scale, MLOps, or delivering datasets to external customers.
Experience with data governance, privacy, or consent programs, including BIPA, GDPR, or CCPA requirements.
Familiarity with warehouse data ecosystems and architectures, including WMS, OMS, WES, and WCS.
Exposure to digital twin or simulation initiatives.
Domain knowledge in warehouse automation, robotics integration, or fulfillment operations.
Experience working with frontier AI companies, robotics companies, academic research institutions, or other emerging technology partners.
