Founding ML Engineer, Computer Vision (Object Detection)

• Design and own computer vision architecture, adapting foundation vision models to specialized catalog identification.
• Define category-level accuracy standards and develop calibrated confidence scoring so the product can communicate uncertainty.
• Build feedback loops that use model errors to guide labeling priorities in partnership with the labeling team.
• Balance broader category coverage against deeper accuracy in existing categories.
• Own the path from model research to production serving, including latency, cost, and reliability.
• Explain model capabilities and tradeoffs to technical and non-technical stakeholders.
• At least 5 years of applied computer vision experience, including production systems delivered at meaningful scale.
• Hands-on experience with fine-grained or instance-level recognition, distinguishing between visually similar items where near-misses are not sufficient.
• Production experience fine-tuning and deploying vision transformers or CNNs with PyTorch or TensorFlow.
• Experience adapting foundation vision models to specific use cases or catalogs.
• Ability to design and run evaluation frameworks that measure model improvements beyond training loss.
• Experience with calibrated confidence scoring and ownership of model serving decisions, including latency, cost, and reliability.
• Strong written and verbal communication, and comfort providing technical leadership on an open-ended problem.

None flagged.