Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.
This is a remote position.
Founding ML Engineer — Computer Vision (Object Detection)
On behalf of an AI commerce tech startup · Remote in North America · Full-time
The build: Our client is an early-stage commerce tech company building production computer vision for precise item identification and pricing. As founding ML engineer, you'll own the identification architecture, data strategy, evaluation standards, and production serving path — end to end, from foundation models fine-tuned toward a specific catalog.
The problem is fine-grained: brand, model, edition, variant. "Close" isn't good enough.
What you'll do
- Design and own the CV architecture for fine-grained item identification — starting from foundation vision models, fine-tuning toward the company's catalog.
- Define what "accurate enough" means per category; build calibrated confidence scoring so the product says "we're not sure" instead of guessing.
- Build the feedback loop between model errors and what gets labeled next, with the labeling lead.
- Decide where to invest: broader category coverage vs. deeper accuracy on today's categories.
- Own the serving path from research to production — latency, cost, reliability at scale.
- Represent the model's capabilities and limits company-wide — including with investors and customers.
Requirements
What we're looking for
- 5+ years applied CV, with at least one system shipped to production at meaningful scale.
- Hands-on fine-grained / instance-level classification — a problem where "close" isn't good enough.
- Fluency in PyTorch or TensorFlow; fine-tuning and deploying vision transformers or CNNs in production.
- Evaluation frameworks for vision models — you know when a model is actually getting better.
- Comfortable being the most senior technical voice on an open-ended problem with no playbook.
- Strong communication — you explain trade-offs to non-technical stakeholders.
Nice to have: resale/marketplace (StockX, GOAT, Vinted, Rebag, The RealReal) or visual search (Pinterest Lens, Google Lens, Syte); active learning / human-in-the-loop pipelines; low-latency API serving at scale; first-ML-hire startup experience.
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