Sieve — Product Operations Lead

Type: Full-time | On-site | San Francisco, CA Compensation: $150,000–$250,000 + Competitive equity Hiring count: 1 Visa sponsorship: Yes — H-1B, OPT Reports to: Founding team (interview loop includes the Chief of Staff and CEO; no named report/LinkedIn on the role page)

About Sieve

Sieve is an AI research lab focused on video data — building exabyte-scale video infrastructure, novel video-understanding techniques, and datasets that push the frontier of video modeling. It works with frontier AI labs and enterprise customers on highly specific dataset problems, building custom algorithms, models, and data pipelines at scale across video, audio, and multimodal data for AI training and evaluation. Its data has earned the trust of frontier AI labs, Fortune 100 companies, and fast-growing generative-AI startups. The team is roughly 25 people, capital-efficient, and shipping directly into the models defining the frontier.

Founded: 2022 | Team size: ~25 (Seed · 11–50) | Stage: Seed Industry: AI Tools / AI data infrastructure Website: sievedata.com Office: San Francisco, CA

Why Candidates Should Join

• Ship into frontier models: Operational work here directly powers the datasets frontier AI labs, Fortune 100s, and top generative-AI startups train on.
• True 01 ownership: Build workforce, QA, and growth workflows from scratch, no playbook — genuine end-to-end ownership of the data-ops platform.
• High-leverage seat on a small team: ~25 people, capital-efficient, reporting into the founding team (Chief of Staff / CEO in the loop).
• Strong comp + perks: $150K–$250K + competitive equity, 401k, full health insurance, all meals covered, and Ubers home.

Intake Call Summary

No intake call transcript was included on the role page.

The Role

Own the day-to-day execution and scaling of Sieve's data-operations platform — the human workforce, vendor partnerships, and QA processes that power it. A scrappy, hands-on role at the intersection of operational execution and platform growth: run acquisition campaigns to expand the user base, build and improve workflows from scratch, and work directly with engineering to ship tooling improvements. Best fit is someone early in their career with strong technical instincts, high communication skills, and startup experience who can move fast without a playbook.

What You'll Be Doing

• Operate and scale the internal data-ops platform, including workforce management, task assignment, and QA workflows
• Drive platform growth by running acquisition campaigns, testing new sourcing channels, and expanding the user base through creative, 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

Tech stack: Not specified. Role involves data tooling, light scripting, and spreadsheet-level analysis; familiarity with ML data pipelines is a plus.

Additional qualification detail from the role body (for scoring context — not in Contrario's parsed Requirements card):

• Bachelor's degree in CS, STEM, or equivalent practical experience
• The 1–5 years is framed as "in a scrappy startup environment, ideally in ops, GTM, data, or a technical adjacent role"

Requirements

• Operate and scale internal data ops platform including workforce management and QA workflows
• Drive platform and partnerships growth through acquisition campaigns and sourcing channels
• Source, onboard, and manage distributed human workforce for data annotation and curation
• Build and improve QA processes for frontier AI lab standards
• Own product ops for data platform and work with engineering on tooling improvements
• Create documentation, SOPs, and training materials for operational workflows
• Mixed technical and non-technical skillset with comfort in data tooling and light scripting
• Strong organizational skills and attention to detail managing multiple concurrent work streams
• 1 to 5 years experience, startup environment preferred
• SF in-person, full-time

Green Flags

• Experience managing human-in-the-loop data operations or annotation pipelines
• At least 1 year of engineering experience or strong technical fluency
• Early hire experience at startup or ops leadership at AI lab
• Familiarity with data quality frameworks or ML data pipelines
• Data space background a strong plus

Red Flags

• No technical grounding whatsoever
• Strategy-only profile with no hands-on execution
• No startup exposure

Role Details

• Salary — $150,000–$250,000
• Equity — Competitive equity
• On-site policy — In-person at Sieve's SF headquarters
• Visa sponsorship — H-1B, OPT
• Employment type — Full-time
• Location — San Francisco, CA
• Experience — 1–5 years

Benefits & Perks

• 401k
• Full health insurance
• Breakfast, lunch, and dinner covered
• Choice of snacks
• Ubers covered home
• Competitive equity

Screening Questions

1. LinkedIn URL
2. Where are you currently based?
3. Are you willing to relocate to San Francisco? If you already live here, please click yes.
4. Will you require sponsorship to work in the United States now or in the future?
5. If so, please describe your current visa status.
6. What is the hardest operational project you've worked on?

Interview Process

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Initial Screen — Initial screen. Stage 3 — Intro Chat with the Chief of Staff — Intro conversation with the Chief of Staff. Stage 4 — Chat with CEO — Conversation with the CEO. Stage 5 — Onsite — On-site interview. Stage 6 — Offer Extended Stage 7 — Candidate Hired — Candidate accepts and starts.