Founding ML Engineer, Computer Vision — Equity & Production-Grade Impact

Moe; New York (NY); Presencial; USD 200 000 - 260 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Competitive cash compensation

Resumo da oferta

Moe is hiring a senior machine learning leader to own the end-to-end identification system that labels and prices items from photos. You will set the technical direction, architecture, data strategy, and the accuracy bar for production-ready models.

You will lead fine-grained, instance-level classification, calibrate confidence scores, and drive the deployment of models behind low-latency APIs while communicating tradeoffs to investors and stakeholders.

Qualificações

• 5+ years in applied computer vision, with at least one system shipped to production at meaningful scale.
• Hands-on experience with fine-grained/instance-level classification beyond general object detection.
• Strong fluency in PyTorch or TensorFlow; experience deploying vision models in production.
• Experience designing and running evaluation frameworks for vision models.

Responsabilidades

• Own the computer vision architecture for fine-grained item identification from day one.
• Define accuracy thresholds per category and build calibrated confidence scoring.
• Build feedback loop between model errors and labeling workflows with the labeling lead.
• Own the model serving path from research to production, balancing latency and cost at scale.

Conhecimentos

Applied CV

Production experience

Fine-grained classification

PyTorch/TensorFlow

Evaluation frameworks

Technical leadership

Strong communication

Ferramentas

Vision transformers

CNNs

Deployment pipelines

Descrição da oferta de emprego

Moe is hiring a senior machine learning leader to own the end-to-end identification system that labels and prices items from photos. You will set the technical direction, architecture, data strategy, and the accuracy bar for production-ready models.

You will lead fine-grained, instance-level classification, calibrate confidence scores, and drive the deployment of models behind low-latency APIs while communicating tradeoffs to investors and stakeholders.