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Founding ML Engineer, Computer Vision (Object Detection)

Skills

apicomputer visionfine tuningmachine learningpytorchtensorflowtransformersTensorFlowPyTorch

Description

About the Role

As the founding machine learning engineer focused on computer vision, you will set the technical direction for identifying items from images in an online marketplace. You will build and productionize models that distinguish fine-grained product details, with a focus on accuracy, calibrated confidence, and earning user trust.

What You'll Do

  • Design the computer vision architecture for fine-grained item identification, starting with foundation vision models and adapting them to a specific catalog.

  • Set accuracy standards by category and build confidence scoring that allows the product to communicate uncertainty.

  • Work with labeling partners to turn model errors into targeted data collection and labeling priorities.

  • Balance expanding category coverage with improving accuracy in existing categories.

  • Own the path from model development to production serving, including latency, cost, and reliability.

  • Explain the model's capabilities and limitations to technical and non-technical stakeholders.

What We're Looking For

  • At least 5 years of applied computer vision experience, including shipping systems to production at meaningful scale.

  • Hands-on experience with fine-grained or instance-level classification, where distinguishing similar items is important.

  • Production experience fine-tuning and deploying vision transformers or CNNs with PyTorch or TensorFlow.

  • Experience building evaluation frameworks, calibrated confidence scoring, and model-serving systems, including low-latency APIs.

  • Experience adapting foundation vision models to specific use cases, plus strong communication skills and comfort leading an open-ended technical problem.

Compensation & Benefits

Salary range: $200,000 to $260,000 USD annually. Visa sponsorship is not available.

Location

Remote within North America, including the United States.

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