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Founding ML Engineer, Computer Vision (Object Detection)

Salary
$200K–$260K
Hiring from
North America
Work type
Remote
Posted
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About the Role

As the founding machine learning engineer, you will set the technical direction for computer vision in an early-stage resale marketplace. You will build and productionize models that identify items from images with the accuracy and confidence needed to support trustworthy pricing and customer decisions.

What You'll Do

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

  • Define accuracy standards by category and develop calibrated confidence scores so the product can recognize when it is uncertain.

  • Build feedback loops that use model errors to guide future labeling and model improvement.

  • Set priorities between expanding category coverage and improving accuracy in existing categories.

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

  • Communicate model capabilities and limitations to technical and non-technical stakeholders.

What We're Looking For

  • At least 5 years of applied computer vision experience, including a system shipped to production at meaningful scale.

  • Experience with fine-grained or instance-level classification, where distinguishing similar items matters.

  • Strong PyTorch or TensorFlow skills, with production experience fine-tuning and deploying vision transformers or CNNs.

  • Experience building evaluation frameworks that measure real-world model performance and improvement.

  • Comfort serving as the senior technical voice on an open-ended problem without an established playbook, and explaining tradeoffs to non-technical stakeholders.

  • Experience with active learning or human-in-the-loop labeling, low-latency model APIs, or an early-stage startup is valuable.

Compensation & Benefits

Annual salary range: $200,000 to $260,000.

Location

Fully remote within North America.

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