Product Operations Lead

The Product Operations Lead role focuses on running and scaling an internal data-operations platform, connecting operational execution with product operations, QA workflows, and workforce management. You will also support platform and partnership growth while sourcing and coordinating a distributed team for data annotation and curation.

Location and Employment

• Location: San Francisco, CA
• Work policy: On-site
• Employment type: Full-time

Compensation

• Salary: USD 150,000 - 250,000 per year
• Additional: Competitive equity
• Visa sponsorship: Available (H-1B, OPT)

Responsibilities

• Operate and scale the internal data-ops platform, including workforce management and QA workflows
• Support platform and partnership growth through acquisition campaigns and sourcing channels
• Source, onboard, and manage a distributed human workforce for data annotation and curation
• Build and improve quality assurance processes aligned to frontier-AI-lab standards
• Own product operations for the data platform and partner with engineering on tooling improvements
• Create documentation, SOPs, and training materials for operational workflows

Requirements

• A mixed technical and non-technical skillset, comfortable with data tooling and light scripting
• Strong organizational skills and attention to detail across multiple concurrent work streams
• Experience: 1–5 years (startup environment preferred)
• On-site availability: San Francisco, full-time

Technologies

• Data tooling
• Light scripting
• Spreadsheet-level analysis

Benefits

• Competitive equity
• 401k
• Full health insurance
• All meals covered
• Rides home

Nice to Have

• Experience managing human-in-the-loop data operations or annotation pipelines
• At least a year of engineering experience or strong technical fluency
• Early-hire experience at a startup, or ops leadership at an AI lab
• Familiarity with data-quality frameworks or ML data pipelines
• A data-space background

Role Focus

This position is responsible for day-to-day execution and scaling of an internal data-operations platform. The work includes workforce management, QA workflow development, product operations for the data platform, and collaboration with engineering to improve tooling that supports annotation and curation at scale.