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Job Description

Founding Machine Learning Engineer, Computer Vision to set the technical direction for fine-grained object detection, calibrated uncertainty, and production serving.

Responsibilities

  • Design and own the computer vision architecture for fine-grained identification, including adapting foundation vision models to a specific catalog
  • Define accuracy standards by category and implement calibrated confidence scores so the product expresses uncertainty rather than guessing
  • Partner with labeling teams to convert model errors into prioritized improvements for future data collection
  • Manage tradeoffs between expanding to new categories and improving accuracy for existing ones
  • Own the end-to-end path from model research to production serving, including focus on latency, cost, and reliability
  • Communicate model capabilities and limitations to both technical and non-technical stakeholders

Requirements

  • 5+ years of applied computer vision experience, including production systems shipped at meaningful scale
  • Industry experience in image or item identification, object recognition, or detection, including fine-grained or instance-level classification where closely related items must be distinguished accurately
  • Hands-on experience fine-tuning and deploying vision transformers or CNNs using PyTorch or TensorFlow
  • Experience adapting foundation vision models, building evaluation frameworks beyond loss metrics, and developing calibrated confidence scoring
  • Experience owning production model serving decisions, including latency, cost, and reliability (familiarity with low-latency APIs is useful)
  • Strong communication skills and the ability to lead technical decisions in an open-ended problem space
  • Plus: experience with active learning, human-in-the-loop labeling, visual search, marketplaces, or an early-stage startup

Technologies

  • PyTorch
  • TensorFlow
  • Vision transformers
  • CNNs

Compensation & Benefits

  • Base salary: USD 200,000 - 260,000 per year
  • Visa sponsorship: not available

Location

  • Hybrid (New York, NY, United States)

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