High-quality video, audio, image, and interaction data for frontier AI. Backed by Y Combinator and AI Grant.
About the role
This role mixes industrial partnerships with hands-on data ops. You’ll open doors into factories and similar environments, then run the workforce, QA, and platform work that turns that access into reliable multimodal data for frontier labs. Strong fits look like industrials consultants, robotics operators, PE ops folks, or early ops hires who’ve already managed human-in-the-loop work and know how to earn trust with plant leaders and ops teams. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Direct relationships into factories, manufacturing, or adjacent industrial environments — or a clear track record of building them
- Mixed technical and non-technical skillset, comfortable with data tooling, light scripting, and spreadsheet-level analysis
- Strong organizational skills and attention to detail, able to manage multiple concurrent work streams
- Growth mindset: experience running or contributing to user acquisition, sourcing campaigns, or platform growth
- Comfort with ambiguous, relationship-heavy work and the unglamorous ops needed to make a partnership live
- Bachelor’s in CS, STEM, or equivalent practical experience, in-person at our SF HQ
More about this role
Sieve is a multimodal lab curating the world’s highest-quality training datasets, spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure with novel multimodal understanding techniques to push the frontier of foundation models. We exist to solve one of the biggest bottlenecks in AI: high-quality training data.
We partner with top AI labs and did $XXM last quarter alone, with a team of roughly 40 people. We raised our Series A from firms including Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
Physical AI needs data from real industrial environments — factories, manufacturing lines, and operations that don’t show up on the open web. Unlocking that access, and running the ops that turn it into lab-grade data, is a core growth constraint. You’ll join early enough to build both the partnerships and the operating system behind them.
This role mixes industrial partnerships with hands-on data ops. You’ll open doors into factories and similar environments, then run the workforce, QA, and platform work that turns that access into reliable multimodal data for frontier labs.
Strong fits look like industrials consultants, robotics operators, PE...
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