About the role
About the Role
Join an early-stage marketplace company as its first machine learning engineer focused on computer vision. You will lead the design and production deployment of fine-grained item identification models, helping users identify products accurately and reliably from photos.
What You'll Do
* Design computer vision architecture for identifying items by brand, model, edition, and variant, adapting foundation vision models to a specific catalog.
* Define category-level accuracy standards and build calibrated confidence scoring so the product can communicate uncertainty appropriately.
* Create feedback loops that use model errors to guide labeling priorities in partnership with the labeling team.
* Set priorities between expanding category coverage and improving accuracy in existing categories.
* Own the model serving path from research through production, balancing latency, cost, and reliability at scale.
* Explain model capabilities, limitations, and technical tradeoffs to company stakeholders.
What We're Looking For
* At least 5 years of applied computer vision experience, including shipping systems to production at meaningful scale.
* Hands-on experience with fine-grained or instance-level recognition, distinguishing visually similar products where near-misses are not acceptable.
* Production experience fine-tuning and deploying vision transformers or CNNs using PyTorch or TensorFlow.
* Experience adapting foundation vision models to specific use cases, and designing evaluation frameworks that measure progress beyond loss metrics.
* Experience with calibrated prediction confidence and ownership of model serving decisions, including latency, cost, and reliability.
* Clear written and verbal communication skills, and comfort leading technical direction on an open-ended problem. Active learning, human-in-the-loop labeling, low-latency APIs, and early-stage startup experience are useful.
Compensation & Benefits
Base salary: $200,000 to $260,000 annually. Visa sponsorship is not available.
Location
Hybrid role based in New York, New York.