Founding ML Engineer, Computer Vision (Object Detection)
Overview
Cleragetclera.comPosted 13 days ago
New York City, NY, US$200k to $260k per yearঅফিসে
Quick apply- চাকরির ধরন
- পূর্ণকালীন
- কাজের অভিজ্ঞতা
- 5+ years
- বিশেষায়ন
- Machine learning & AI
- ভিসা স্পনসরশিপ
- এই পদের জন্য স্পনসরশিপ দেওয়া হয় না
পদ সম্পর্কে
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.