cantina · Singapore
Skills: Python, AWS, Docker
About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. About the Role: Cantina is expanding, and we're looking for an ML Engineer to join our growing Singapore team! In this role, you will build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. You'll own the full pipeline — from raw content to curated, filtered, and training-ready datasets — with a focus on speed, reliability, reproducibility, and cost-efficiency. You'll partner closely with curation and modeling teams to operationalize evolving dataset recipes and iterate on approaches that improve model outcomes. What You’ll Do: - Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes - Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs - Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems - Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency - Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns - Design and implement curation pipelines that determine which video and image content is selected, filtered, and retained for model training, including image-text pair datasets used in joint training regimes - Build and improve VLM-based captioning and metadata generation workflows at scale across both video and image data - Develop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction app…
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