Founding ML Engineer, Computer Vision (Item Identification)

About the roleMoe's entire pitch to users rests on one claim: we can identify any item in the world from a photo and price it as accurately as a human expert, instantly. You'll build the model that makes that claim true. This isn't a research exercise — every category you get right becomes a category users trust Moe with real money, and every one you get wrong becomes a support ticket and a churn risk. You'll set the technical direction for identification from day one, with real ownership over architecture, data strategy, and the accuracy bar we hold ourselves to.What you'll doDesign and own the computer vision architecture for fine-grained item identification — brand, model, edition, variant — starting from foundation vision models and fine-tuning toward Moe's specific catalogDefine what "accurate enough" means per category, and build calibrated confidence scoring so the product can say "we're not sure" instead of guessingBuild the feedback loop between model errors and what gets labeled next, in partnership with the labeling leadDecide where to invest: broader category coverage vs. deeper accuracy on today's categoriesOwn the model serving path from research to production — latency, cost, and reliability at scaleRepresent the identification model's capabilities and limits to the rest of the company, including in investor and customer conversations when neededWhat we're looking for5+ years in applied computer vision, with at least one system shipped to production at meaningful scaleHands-on experience with fine-grained/instance-level classification, not just general object detection — you've worked on a problem where "close" isn't good enough (e.g., telling two similar sneaker colorways or watch references apart)Strong fluency in PyTorch or TensorFlow, and experience fine-tuning and deploying vision transformers or CNNs in productionExperience designing and running evaluation frameworks for vision models — you know how to measure whether a model is actually getting better, not just achieving a lower lossComfortable being the most senior technical voice on a hard, open-ended problem with no existing internal playbookStrong written and verbal communication — you'll need to explain technical tradeoffs to non-technical stakeholders, including investorsNice to havePrior work at a resale/marketplace company (StockX, GOAT, Vinted, Rebag, The RealReal) or a visual search company (Pinterest Lens, Google Lens, Syte)Experience with active learning or human-in-the-loop labeling pipelinesFamiliarity with deploying models behind low-latency APIs at scaleFounding or early-stage startup experience, ideally as the first ML hireWhat success looks like30 days: fully ramped on the current state of the identification problem; has picked the first category to ship (e.g., sneakers or watches) and defined the accuracy bar for it60 days: v1 identification model is live for that category, with a measured accuracy baseline against a held-out test set90 days: confidence scoring is in place, and there's a clear, prioritized plan for expanding into the next 2–3 categoriesProcessIntro call → technical deep dive on a past project → a scoped take-home or pairing session on a real Moe identification problem → founder conversation → offerComp: $200K–$260K base + 0.75%–1.5% equity (negotiable for the right candidate)