Cantina

Machine Learning Engineer

• Own model deployment end-to-end – take video AI models from research to production, build robust inference endpoints, optimize performance, and ensure models scale.
• Build production-grade inference pipelines – design, deploy, and maintain ML services that handle real-time video processing; debug complex issues, optimize latency, and ensure 99.9% uptime.
• Engineer video data workflows – build scalable preprocessing pipelines using serverless GPU infrastructure (RunPod, etc.) to transform raw video and audio into model-ready formats.
• Architect cloud-native ML systems – leverage AWS (S3, DynamoDB, Lambda, ECS) and Kubernetes to build resilient, scalable data and inference infrastructure that can handle terabytes of video data.
• Automate data annotation at scale – build and maintain labeling pipelines using AWS Ground Truth and Mechanical Turk.
• Collaborate across teams – work closely with research and product teams to align model requirements and user needs.

Machine Learning Engineer, Video