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Founding ML Engineer, Computer Vision (Object Detection)
remote · Remote · FullTime · Engineering
$200K – $260K
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About the role
Computer VisionMachine LearningPyTorchTensorFlow
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.