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Open nowPosted 244 days ago

Product Operations Lead

Sieve15 open roles

Where
San Francisco
Work mode
On site
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Your applicationOpen nowProduct Operations LeadSieve · San Francisco

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Sieve's own form.

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. Sieve postings stay open a median of 22 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.8%30 days
  6. 59.1%60 days
This job: posted 244 days ago

Sieve median: 22 days open

The posting

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 https://matrix.vc/, Swift Ventures https://www.swift.vc/, Y Combinator https://www.ycombinator.com/, and AI Grant https://aigrant.com/.

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

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