Sieve — Research & Product Lead

Type: Full-time | On-site | San Francisco, CA Compensation: $150,000–$250,000 + Competitive equity Hiring count: 3 Visa sponsorship: None available Reports to: Not specified (interview loop includes Product Ops and CEO)

Sieve is the only AI research lab exclusively focused on video data. Video already accounts for ~80% of internet traffic and has become the dominant medium for creativity, communication, gaming, AR/VR, and robotics — and truly modeling it depends on high-quality training data, which is what Sieve builds. They combine exabyte-scale video infrastructure, novel video-understanding techniques, and dozens of diverse data sources to produce datasets that push the frontier of video modeling, earning the trust of frontier AI labs, Fortune 100s, and fast-growing generative-AI startups. They did multiple millions in revenue last quarter alone with a team of ~15.

Founded: 2022 | Team size: ~15 | Stage: Series A (Matrix Partners, Swift Ventures, Y Combinator, AI Grant) | Total funding: Not disclosed Industry: AI Tools / Video Data Website: sievedata.com Office: San Francisco, CA

• Frontier customer base: Work directly with the most technically demanding organizations in the world — frontier AI labs and Fortune 100s.
• Genuine generalist scope: A role that spans product, applied research, customer engagement, and operations — high leverage, real company-level exposure.
• Early and proven: ~15-person team already doing multiple millions in quarterly revenue, scaling fast.

• Not available — intake exists as a video on the role page; no transcript was provided.

Sieve is hiring a Research & Product Lead to be a high-leverage generalist operator across the company — combining deep technical fluency with strong relationship skills to manage Sieve's most important customer accounts, drive cross-functional execution, and contribute wherever the company needs leverage. It sits at the intersection of product, applied research, customer engagement, and operations. The role exists because Sieve's customers are the most technically demanding organizations in the world and the team is scaling fast; managing those relationships and the operations behind them requires someone who can operate as a peer with the engineers and researchers they work with while building the systems that let the company scale.

The team is open to two profiles:

• Senior track: experienced operators with existing AI-lab relationships or a background at a human-data company (Scale, Surge, Sapien, Mercor, Invisible, Toloka, Snorkel, Defined.ai, or similar).
• Generalist track: 1–2 years out of consulting (MBB or top tier), banking, or as an APM at an AI company, paired with an engineering degree and demonstrable technical curiosity.

Across both tracks, the universal filter is high curiosity and high EQ.

• →Build and manage relationships with frontier AI labs and highly technical customers
• →Translate customer needs into internal product and research priorities
• →Operate as a strategic partner to both external customers and internal technical teams
• →Drive cross-functional execution across research, product, operations, and partnerships
• →Identify operational bottlenecks and implement scalable systems and processes
• →Support high-priority strategic initiatives across the company
• →Work closely with researchers, engineers, and leadership on ambiguous, fast-moving projects
• →Navigate technical conversations around datasets, model performance, infrastructure, and AI workflows
• →Help shape how Sieve scales customer engagement and internal operations as it grows
• →Contribute wherever needed as a high-leverage generalist operator

Tech stack: Not specified

• 1-2 years experience, BS in CS or equivalent
• ML/AI workflow fluency, especially data pipelines
• Peer-level technical depth with frontier lab researchers
• High curiosity, high EQ, strong comms
• Two tracks: senior (AI labs/human data) + generalist (consulting/banking/APM + eng degree)

• Existing trusted relationships at frontier AI labs or AI model companies, direct ecosystem trust is the highest-signal background for the senior track.
• Background at a human data company (Scale, Surge, Sapien, Mercor, Invisible, Toloka, Snorkel, Defined.ai). Direct adjacency to Sieve's market and customer base.
• For the junior track: 1-2 years out of consulting (MBB or top tier), banking, or as an APM at an AI company, paired with an engineering degree. Scrappy, high curiosity, high EQ.
• Comfortable debugging a data pipeline question and navigating a commercial expansion conversation in the same hour. Treats relationship management as a craft.
• Prior early-stage startup experience, especially as an early hire who saw a customer-facing function get built from zero.

• Pure account manager or CSM background with no technical depth. Cannot hold a peer-level conversation with researchers at a frontier lab.
• Pure technical operator with no client-facing or commercial instinct. The role is half relationship, half technical.
• Career enterprise SaaS sales without AI/ML domain context. The buyer is too technical for product framing alone.
• Cannot or will not work 5 days/week in person at Sieve SF HQ. Non-negotiable.
• For the junior track: 1-2 YoE without an engineering degree or without demonstrable technical curiosity. Must be able to ramp into ML/AI workflow conversations quickly.

Salary$150,000–$250,000EquityCompetitive equityOn-site policyIn person at Sieve SF HQ, 5 days/week (non-negotiable)Visa sponsorshipNone availableEmployment typeFull-timeLocationSan Francisco, CA

(Contrario "Required Candidate Q&A" — asked on the call.)

1. LinkedIn URL
2. Where are you currently based?
3. Are you willing to relocate to San Francisco? (If you already live here, answer 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?

Stage 1 — Pending Approval — Submission awaiting Contrario/company approval. Stage 2 — Application Review — Application reviewed by the company. Stage 3 — Initial Screen — First screening conversation. Stage 4 — Chat with Product Ops — Conversation with the Product Ops team. Stage 5 — Chat with CEO — Conversation with the CEO. Stage 6 — Onsite — Onsite interview at SF HQ. Stage 7 — Offer Extended Stage 8 — Candidate Hired — Candidate accepts and starts.

Updated June 26, 2026 Target companies — Databricks, Scale AI, Intrinsic (3 companies shown; no additional collapsed companies on the role page.)

For reference only — do not source these specific profiles (page marks them "DO NOT CONTACT"). LinkedIn URLs were not exposed in the page markup.

• Ibrahim Salami
• Mahek M.
• Arthita Ghosh
• William Tanassi-Dreyer
• Ananyaa Jain
• Iliya Shadfar
• Solomon Kong
• Mohit P
• Timothy Neil Tan
• Geoffrey Jing

• Not available — the activity log notes one candidate approved and one rejected by the company, but no feedback text was provided.