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.
Why NowPhysical 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.
About the RoleThis 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.
What You’ll DoOpen industrial access: Identify, pitch, and close partnerships with factories, manufacturers, and industrial operators; navigate security and compliance from first intro through live collection.
Operate the data ops platform: Run workforce management, task assignment, and QA workflows; build SOPs and training so quality stays at the bar frontier labs require.
Grow supply and partnerships: Source, onboard, and manage a distributed workforce for annotation, curation, and review; test acquisition and sourcing channels that scale.
Own product ops with engineering: Ship tooling improvements, track operational metrics, and close gaps in the data platform.
Turn one-offs into systems: Document playbooks so partner wins and ops workflows repeat across sites and programs.
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
Experience managing human-in-the-loop data operations or annotation pipelines
At least 1 year of engineering experience or strong technical fluency
Prior work inside manufacturing, robotics deployment, factory ops, PE portfolio ops, or as an early hire / AI-lab ops lead
Familiarity with data quality frameworks, ML data pipelines, or physical AI
401(k) and full health insurance
Breakfast, lunch, and dinner covered, plus your choice of snacks
Ubers covered home
*All roles at Sieve require you to be onsite in San Francisco 5 days per week
What We Do
Sieve is the only AI research lab exclusively focused on video data. Video already makes up 80% of internet traffic and has become the dominant medium driving creativity, communication, gaming, AR/VR, and robotics. Unlocking the ability to truly model video is the key to breakthroughs across all of these domains but progress has been bottlenecked by one thing: high-quality training data. That’s where Sieve comes in. We bring together exabyte-scale video infrastructure, novel video understanding techniques, and dozens of diverse data sources to create datasets that push the frontier of video modeling. This unique combination allows us to deliver data with unmatched precision, quality, and speed which has earned the trust of frontier AI labs, Fortune 100 companies, and fast-growing generative AI startups.
