Founding ML Engineer — Computer Vision (Object Detection)
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Job Summary
This is a remote position.
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 youll 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 isnt good enough.
What youll do
- Design and own the CV architecture for fine-grained item identification starting from foundation vision models fine-tuning toward the companys catalog.
- Define what accurate enough means per category; build calibrated confidence scoring so the product says were 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 todays categories.
- Own the serving path from research to production latency cost reliability at scale.
- Represent the models capabilities and limits company-wide including with investors and customers.
What were 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 isnt 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.
Required Skills:
What Were Looking For: 5 years in applied computer vision with at least one system shipped to production at meaningful scale Hands-on experience with fine-grained / instance-level classification not just general object detection youve worked on a problem where close isnt 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 production Experience 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 loss Comfortable being the most senior technical voice on a hard open-ended problem with no existing internal playbook Strong written and verbal communication youll need to explain technical tradeoffs to non-technical stakeholders including investors Nice to Have Prior 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 pipelines Familiarity with deploying models behind low-latency APIs at scale Founding or early-stage startup experience ideally as the first ML hire This is a REMOTE job anywhere in North America.
Required Education:
Bachelors degree from an accredited college or university is preferred but not required.
