High-quality video datasets for AI labs
Sieve is an AI research lab focused on video data, sourcing petabytes of video, filtering and indexing it with advanced techniques, and delivering high-quality training datasets. They serve frontier AI labs, Fortune 100 companies, and generative AI startups working on video modeling for applications in creativity, gaming, AR/VR, and robotics. This addresses the data bottleneck in advancing video AI capabilities.
Deployed Research Engineer works directly with AI labs to build custom algorithms, models, and pipelines for high-quality video datasets at scale. Requires strong Python/PyTorch skills, customer collaboration, and end-to-end delivery from prototypes to production.
Owns execution and scaling of data operations platform, managing workforce, QA processes, partnerships growth, and product ops initiatives. Requires mixed technical skills, organizational strength, growth mindset, and bachelor's in CS/STEM.
Designs and maintains reliable infrastructure for petabyte-scale video processing across multi-cloud environments. Owns incident response, observability (Prometheus, OpenTelemetry), security, and CI/CD tooling with 3+ years experience in scalable systems.
Builds and scales a full-stack video collection platform with mobile features for user submissions, internal tools, and high-reliability systems. Requires strong TypeScript/React frontend, React Native mobile, Python/Go backend experience, and high ownership in a fast-paced startup.
Leads relationships with top AI lab customers, serving as technical contact to manage accounts, drive revenue expansion, and translate needs into engineering priorities. Requires deep ML/AI knowledge, strong networks in AI labs, and customer-facing technical experience.
Builds and scales data pipelines for video AI datasets, owning end-to-end projects from sourcing to delivery with ML filters and dashboards. Requires strong Python skills, clean code practices, and customer communication.
Builds high-performance pipelines and building blocks for video understanding at scale using computer vision, audio processing, and ML optimization techniques. Requires 2+ years in CV/audio, strong Python/PyTorch skills, and passion for video tech.