About the role
About Us
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.
Why Now
Sieve is one of the most capital-efficient teams in AI — roughly 30 people serving the world's leading AI labs across every major data modality. You'll join early, own problems end-to-end, and watch your work ship directly into the models defining the frontier.
About the Role
As Product Operations Lead, you'll own the day-to-day execution and scaling of Sieve's data operations platform alongside vendor partnerships. This is a deeply operational and semi-technical role. You'll manage our human workforce, build and improve QA processes, handle people sourcing and onboarding, and drive product ops initiatives that make our platform more efficient. A major part of this role is growth: you'll run campaigns and experiments to expand the platform's user base, find new channels for sourcing, and drive adoption. This role is ideal for someone who is both a builder and an optimizer, someone who can get their hands dirty with tooling while also thinking strategically about how to scale a complex operational machine.
What You'll Do
- Operate and scale Sieve's internal data ops platform, including workforce management, task assignment, and QA workflows
- Drive platform and partnerships growth: run acquisition campaigns, test new sourcing channels, and grow the user base through creative and scalable strategies
- Source, onboard, and manage a distributed human workforce for data annotation, curation, and quality review
- Build and improve QA processes to ensure data output meets the standards required by frontier AI labs
- Own product ops for the data platform. Work with engineering to ship tooling improvements, track operational metrics, and identify gaps
- Create documentation, SOPs, and training materials for operational workflows
Requirements
- 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 efforts
- Bachelor's degree in CS, STEM, or equivalent practical experience
- In-person at our SF HQ
Nice to Have
- Experience managing human-in-the-loop data operations or annotation pipelines
- At least 1 year of engineering experience or strong technical fluency
- Experience as an early hire at a startup or spearheading ops at an AI lab
- Familiarity with data quality frameworks or ML data pipelines
Benefits
- 401k + Full Health Insurance
- Breakfast, Lunch, and Dinner covered and your choice of snacks
- Ubers covered home