Founding ML Engineer, Computer Vision

Responsibilities

• Design computer vision architectures for fine-grained item identification using foundation vision models adapted to the marketplace catalog.
• Set category-specific accuracy standards and build calibrated confidence scoring to communicate prediction uncertainty.
• Develop model-error feedback loops to prioritize future labeling with the labeling team.
• Balance category coverage expansion against accuracy improvements in existing categories.
• Own the transition from model development to production serving while managing latency, cost, and reliability.
• Explain model capabilities, limitations, and technical tradeoffs to technical and non-technical stakeholders.

Requirements

• At least 5 years of applied computer vision experience, including shipping a production system at meaningful scale.
• Hands-on experience with fine-grained or instance-level classification involving visually similar items.
• Strong fluency in PyTorch or TensorFlow and practical experience fine-tuning and deploying vision transformers or CNNs.
• Experience designing and running vision model evaluations beyond loss metrics.
• Experience owning model serving decisions involving latency, cost, reliability, and calibrated confidence scoring.
• Ability to take technical ownership of open-ended problems and communicate tradeoffs clearly.
• Experience with active learning, human-in-the-loop labeling, low-latency model APIs, or an early-stage startup is a plus.

Benefits

• Annual base salary of $200,000 to $260,000.
• On-site role in San Francisco, California, United States.