Sieve
Recruiting
San Francisco · full-time
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odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
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About the role
- This is a high-ownership role for someone who thrives in ambiguity, moves fast without sacrificing quality, and wants to be directly responsible for scaling a high-growth startup.
Responsibilities
- As our first recruiter at Sieve, you'll build our recruiting function from the ground up and hire across engineering and our product operations org.
- No day will look the same - ideal candidates will bounce around between sourcing candidates, taking screen calls, running recruiting events, and anything else that's needed to build the most talent dense organization.
Requirements
- At least 3 years of technical recruiting experience
- Familiarity with AI/ML/Research talent market
- Experience as a founding recruiter
- Experience with hosting recruiting events
- Experience with AI tools (Claude, Cursor, etc)
Compensation
- $120k-$200k
Benefits
- 401k + Full Health Insurance
Company info
- Sieve is the only AI research lab exclusively focused on video data.
- We combine exabyte-scale video infrastructure, novel video understanding techniques, and dozens of data sources to develop datasets that push the frontier of video modeling.
- Video makes up 80% of internet traffic and has become the enabling digital medium powering creativity, communication, gaming, AR/VR, and robotics.
- Sieve exists to solve the biggest bottleneck in growth of these applications: high-quality training data.
- We've partnered with top AI labs and did $XXM last quarter alone, as a team of just 15 people.
- We also raised our Series A last year from Tier 1 firms such as Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
- Sieve exists to solve the biggest bottleneck in growth of these applications:
- high-quality training data.
This listing is sourced directly from Sieve's careers page and normalized into a canonical job model.