Company Overview:We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.The OpportunityWe’re hiring a Product Manager, Data Lab to sit at the center of Protege’s research and innovation engine.This role exists to translate cutting-edge AI research and experimentation into scalable product capabilities — ensuring that the tools, workflows, and systems our Data Lab uses are aligned with how modern AI models are actually trained, evaluated, and deployed.You will work closely with research scientists, applied ML engineers, and product teams to:accelerate experimentationimprove reproducibility and iteration velocityand decide which research outputs should become real, durable product featuresThis is a role for someone who understands frontier AI deeply, but chooses to apply that understanding through product judgment rather than research authorship.What You’ll DoProductize Frontier AI WorkflowsPartner closely with Data Lab scientists to understand how models are being trained, evaluated, and iterated todayTranslate experimental workflows (data curation, labeling, evaluation, fine-tuning, feedback loops) into scalable product and platform capabilitiesIdentify patterns across experiments that are worth standardizing versus those that should remain bespokeBuild Tools That Reflect How AI Is Actually BuiltLead product discovery and execution for internal tools that support modern AI development:dataset versioningevaluation pipelinesannotation and human-in-the-loop workflowsexperiment tracking and reproducibilityEnsure tooling reflects real-world frontier practices, not academic abstractionsBe a Bridge Between Research and ProductServe as the primary product interface for the Data LabTranslate research intuition into product requirements engineers can build againstHelp researchers reason about tradeoffs between novelty, robustness, and scalabilityCollaborate with Platform and Vertical PMs to ensure new capabilities integrate cleanly into customer-facing productsExercise Strong Product JudgmentDecide when an experimental capability is ready to move from “research mode” to “product mode”Apply an 80/20 mindset without undermining scientific rigorSunset or deprioritize tools and ideas that do not meaningfully advance AI development velocity or data qualityMeasure Impact, Not ActivityDefine success metrics tied to:experiment cycle timeresearcher productivityadoption of internal toolsdownstream impact on customer data productsUse qualitative and quantitative feedback to continuously iterateWho You AreDeeply Fluent in Modern AIYou have hands-on or adjacent experience with how frontier AI models are built today — including large-scale training, fine-tuning, evaluation, and data iterationYou understand concepts like:training data quality vs quantity tradeoffsevaluation benchmarks vs real-world performancehuman feedback loopsmultimodal data challengesYou can have credible conversations with PhD-level researchers and senior ML engineersA Product Thinker, Not a ResearcherYou don’t need to publish papers — but you need to understand themYou excel at turning complex technical systems into clear product decisionsYou enjoy asking: “What problem does this actually solve, and at what scale?”Experienced Product Manager5+ years of product management experience, ideally in:AI/ML platformsdeveloper toolsdata infrastructureor internal research toolingStrong experience working with highly technical stakeholdersProven ability to lead ambiguous, zero-to-one initiativesCollaborative and High-AgencyExcellent communicator across research, engineering, and productComfortable influencing without authorityBias toward shipping, learning, and iteratingNice to HavePrior experience working with or adjacent to frontier model buildersExperience with multimodal AI systems (text, audio, video, healthcare data)Background in ML engineering, data science, or applied research before PMWhy ProtegeWork directly on the infrastructure powering frontier AI developmentPartner with world-class researchers and product leadersShape how experimental AI capabilities become scalable, real-world productsCompetitive compensation, equity, and benefits