AI Context & Knowledge Infrastructure Lead

Mercury; San Francisco (CA); On-site; USD 163,000 - 203,800; Full time

Benefits offered by this job

Stock options

Benefits

Job summary

Mercury in San Francisco is seeking a leader who will own the internal knowledge infrastructure—the trusted context layer that captures what teams own, are building, and know. You’ll define information architecture, taxonomy, and governance to keep data accurate, current, and useful across systems and AI tools.

You’ll partner with Engineering to design schemas, automations, and validation workflows, and own the reporting layer that turns shared context into leadership dashboards and planning

Qualifications

• 5–8 years of experience in program or product operations, technical program management, product management, data, or similar roles.
• Able to turn messy, distributed information into simple, scalable structures.
• Comfortable with APIs, data models, analytics, and tools like Linear, GitHub, Metabase.
• Hands-on experience using AI to create leverage through workflows and automations.
• Influence organizations through clear communication and execution.

Responsibilities

• Own Mercury's knowledge infrastructure: the trusted context layer and information architecture.
• Build the knowledge layer on Mercury's AI infrastructure with schemas, automations, and validation workflows.
• Own the reporting layer turning context into leadership reporting and planning dashboards.
• Drive adoption of standardized systems by partnering with multiple teams to replace fragmented documentation.
• Continuously improve knowledge capture, organization, and use for humans and AI.

Skills

Program operations

Product operations

Technical program management

Knowledge architecture

Systems design

Tools

Linear

GitHub

Metabase

AI platforms

Job description

Mercury in San Francisco is seeking a leader who will own the internal knowledge infrastructure—the trusted context layer that captures what teams own, are building, and know. You’ll define information architecture, taxonomy, and governance to keep data accurate, current, and useful across systems and AI tools.

You’ll partner with Engineering to design schemas, automations, and validation workflows, and own the reporting layer that turns shared context into leadership dashboards and planning