Partnerships & Operations Lead
Owns execution and scaling of data operations platform, managing workforce, QA processes, partnerships growth, and product ops initiatives. Requires mixed technical skills, organizational strength, growth mindset, and bachelor's in CS/STEM.
About the job
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
Skills
Data Tooling, Light Scripting, Spreadsheet Analysis, Qa Processes, User Acquisition, Sourcing Campaigns, Workforce Management, Operational Metrics, Data Annotation, Ml Data Pipelines
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