Job Description

Product Operations Lead

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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