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Curates and evaluates image and video sources for video generation and multimodal AI model development. The role translates research priorities into sourcing criteria, assesses visual quality and relevance, maintains source catalogues and metadata, identifies dataset gaps, documents provenance and licensing, and develops repeatable curation workflows. Strong visual judgment, research ability, organization, communication, and familiarity with visual media are required; machine learning experience is not necessary.

Conduct research on next-generation video generation models, focusing on distillation, inference efficiency, reward modeling, preference optimization, evaluation, and scalable training infrastructure. Interns will formulate hypotheses, run large-scale experiments, analyze results, develop research tooling, and communicate findings through reports, presentations, demonstrations, and potential conference submissions under senior mentorship.

Three-month onsite internship developing and evaluating video-generation machine learning systems. Projects may involve reward modeling, inference efficiency, multimodal data pipelines, distributed training infrastructure, large-scale experimentation, and evaluation tooling. Interns own a defined research or engineering project with senior mentorship and may contribute to conference publications or product-adjacent work.

The ML Engineer will build and scale large-scale data systems and pipelines, focusing on workload orchestration, dataset curation, and quality improvement techniques for machine learning models.