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Founding ML Engineer, Computer Vision (Object Detection)

Overview
Cleragetclera.comPosted 13 days ago
New York City, NY, US$200k to $260k per yearकार्यालयातून
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नोकरीचा प्रकार
पूर्णवेळ
कामाचा अनुभव
5+ years
विशेषीकरण
Machine learning & AI
व्हिसा प्रायोजकत्व
या भूमिकेसाठी प्रायोजकत्व दिले जात नाही

भूमिकेबद्दल

As the founding machine learning engineer, you will set the technical direction for fine-grained visual item identification at an internet marketplace platform. You will build production models that identify specific brands, models, editions, and variants, helping users make informed decisions with confidence.

Responsibilities

  • 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.

Key skills

  • Machine Learning
  • Computer Vision
  • PyTorch
  • TensorFlow

Benefits & perks

Base salary: $200,000 to $260,000 annually. Visa sponsorship is not available.

  • Remote friendly

What We're Looking For

  • 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.

Location

Hybrid role based in New York, New York.