High-quality video, audio, image, and interaction data for frontier AI. Backed by Y Combinator and AI Grant.
About the role
As a Member of Technical Staff, Applied Research at Sieve , you’ll train and evaluate multimodal models to understand how data shapes their capabilities. Your work will span video generation and audiovisual understanding, connecting advances in data curation with measurable improvements in model performance.
What they're looking for
- 2+ years of experience in machine learning research or engineering, with hands-on experience training or fine-tuning deep learning models
- Strong Python and PyTorch skills, including the ability to implement, debug, and modify model training code
- Experience designing experiments, establishing baselines, and evaluating results critically
- Familiarity with modern generative or multimodal architectures, such as diffusion models or transformers
- Comfortable working with large datasets and diagnosing training bottlenecks, instability, and data quality issues
- Able to turn ambiguous research questions into concrete experiments and maintainable systems
More about this role
Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data.
We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as Matrix Partners , Swift Ventures , Y Combinator , and AI Grant .
Sieve combines access to diverse multimodal data, infrastructure to process it at scale, and close relationships with the teams building frontier models. This gives us a unique opportunity to study what makes training data effective—and turn those findings into better models and datasets.
You’ll join a small team building our research capabilities, with ownership over experiments, training systems, and the decisions those results inform.
As a...
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