Member of Technical Staff, Applied Research

Sieve is a multimodal research lab curating high-quality training datasets across video, audio, images, text, and 3D. The Member of Technical Staff, Applied Research will train and evaluate multimodal models, develop reproducible training and evaluation pipelines, and run controlled experiments to understand how data affects model capabilities. The role also turns research findings into improvements in data curation pipelines and products while collaborating with internal teams and partner labs.

Responsibilities:

• Train and post-train models for video generation and multimodal understanding
• Design controlled experiments to measure how data selection, mixtures, and supervision affect model capabilities
• Build evaluations that reveal specific model weaknesses, using quantitative metrics and human judgment
• Develop reliable training infrastructure, including distributed training, efficient data loading, checkpointing, and experiment tracking
• Turn research findings into improvements in our data curation pipelines and products
• Collaborate with research and engineering teams internally and at partner labs to define meaningful problems and communicate results