Research & Product Lead

Research & Product Lead

San Francisco, CA
· On-site
· Full-time
Compensation: $150,000–$250,000 + competitive equity

An AI research lab focused on video data — building the high-quality training datasets that frontier video models depend on. The team combines large-scale video infrastructure, novel video-understanding techniques, and diverse data sources to deliver datasets trusted by frontier AI labs, Fortune 100s, and fast-growing generative-AI startups. Already generating multiple millions in quarterly revenue with a small, fast-scaling team.
Founded 2022 · ~15 people
· Industry: AI Tools / Video Data

A high-leverage generalist operator role at the intersection of product, applied research, customer engagement, and operations. You'll manage the company's most important customer accounts, drive cross-functional execution, and contribute wherever the company needs leverage — equally comfortable in a technical deep-dive, shaping roadmap priorities, navigating a commercial conversation, or building scalable internal systems.

The team is open to two profiles:

• Senior track: experienced operators with existing AI-lab relationships or a background at a human-data company.
• 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.

What you'll be doing

• 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
• 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 the company'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 the 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.

• Work directly with the most technically demanding organizations in the world — frontier AI labs and Fortune 100s
• Genuine generalist scope across product, applied research, customer engagement, and operations
• Early-stage, high-ownership role on a small team already generating real revenue