Technical Program Manager

Labelbox•San Francisco, CA

16d•$140,000 - $170,000•Hybrid

About The Position

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling

Requirements

Experience running operational projects or programs with many moving parts and people.
Strong organizational and tracking discipline — comfortable owning dashboards, sheets, and a system of record such as Airtable.
Operational and financial literacy: ease with throughput metrics, margin, and billing/AR.
Ability to coordinate and orchestrate multiple stakeholders — engineers, researchers, and a contingent workforce through its leads — and keep them aligned.
A bias toward action, calm under pressure, and a habit of catching problems before they become delivery issues.
Comfort operating in ambiguous, fast-scaling environments where processes are still being built.
The ability to manage multiple projects simultaneously without losing attention to detail.

Nice To Haves

Familiarity with AI, machine learning, data pipelines, RLHF, or data-labeling/annotation operations.
Experience managing contractor or contingent workforces.
Experience with Airtable or similar operational tooling.
Experience owning billing, margin, or financial tracking for projects.

Responsibilities

Run assigned projects day-to-day, owning throughput, delivery, and operational quality from kickoff through delivery; a TPM is staffed on any project with a meaningful number of data trainers.
Run each project through its gates: calibrate before scaling, ramp in stages while quality holds, and meet acceptance criteria at delivery — so quality is enforced in flight, not discovered at the end.
Set up projects and bring data trainers on using the platform tooling, configuring the right setup for different project types.
Keep Airtable accurate as the system of record — tasks and status always current — owning tracking across internal projects (fulfillment, data-trainer tracking, sheets) and external projects (glide-path templates and margin tracking).
Own project-level financials: track margin and fulfillment live, operate payment tiers, handle project billing/AR, and resolve data-trainer payment issues — flagging margin risk early and escalating margin-protecting changes to the comp structure to the Deployment Lead, who owns account-level margin.
Keep the FDE and FDR connected as the project kicks off, so technical scoping and research inputs land in the work.
Orchestrate the data-trainer workforce through Pod Leads — keeping trainers productive, paid, and organized, and using Pod-Lead first-line review as early quality signal — rather than managing hundreds of trainers directly.
Identify delivery risks early, put mitigations in place, and escalate when throughput, quality, or timelines are at risk.
Work flexibly across projects and domains as priorities shift.