Founding ML Engineer, Computer Vision (Object Detection)

• Design computer vision architecture for fine-grained item identification, building on foundation models and adapting them to a specific catalog. • Define accuracy standards by category and develop calibrated confidence scores so the product can recognize when it is uncertain. • Build feedback loops that use model errors to guide future labeling and model improvement. • Set priorities between expanding category coverage and improving accuracy in existing categories. • Own the path from research to production, including model serving latency, cost, and reliability. • Communicate model capabilities and limitations to technical and non-technical stakeholders.

• At least 5 years of applied computer vision experience, including a system shipped to production at meaningful scale. • Experience with fine-grained or instance-level classification, where distinguishing similar items matters. • Strong PyTorch or TensorFlow skills, with production experience fine-tuning and deploying vision transformers or CNNs. • Experience building evaluation frameworks that measure real-world model performance and improvement. • Comfort serving as the senior technical voice on an open-ended problem without an established playbook, and explaining tradeoffs to non-technical stakeholders. • Experience with active learning or human-in-the-loop labeling, low-latency model APIs, or an early-stage startup is valuable.

About the Role As the founding machine learning engineer, you will set the technical direction for computer vision in an early-stage resale marketplace. You will build and productionize models that identify items from images with the accuracy and confidence needed to support trustworthy pricing and customer decisions. What You'll Do • Design computer vision architecture for fine-grained item identification, building on foundation models and adapting them to a specific catalog. • Define accuracy standards by category and develop calibrated confidence scores so the product can recognize when it is uncertain. • Build feedback loops that use model errors to guide future labeling and model improvement. • Set priorities between expanding category coverage and improving accuracy in existing categories. • Own the path from research to production, including model serving latency, cost, and reliability. • Communicate model capabilities and limitations to technical and non-technical stakeholders. What We're Looking For • At least 5 years of applied computer vision experience, including a system shipped to production at meaningful scale. • Experience with fine-grained or instance-level classification, where distinguishing similar items matters. • Strong PyTorch or TensorFlow skills, with production experience fine-tuning and deploying vision transformers or CNNs. • Experience building evaluation frameworks that measure real-world model performance and improvement. • Comfort serving as the senior technical voice on an open-ended problem without an established playbook, and explaining tradeoffs to non-technical stakeholders. • Experience with active learning or human-in-the-loop labeling, low-latency model APIs, or an early-stage startup is valuable. Compensation & Benefits Annual salary range: $200,000 to $260,000. Location Fully remote within North America. Originally posted on Himalayas Founding-ML-Engineer, Founding-AI-Engineer, Computer-Vision-Engineer