Knowledge Systems Architect

Upside•Austin, TX

17h•Hybrid

About The Position

Upside is seeking a Knowledge Systems Architect to own the infrastructure layer for AI adoption, making the organization legible to machines and itself. This role focuses on designing systems that enable content to create itself, be maintained automatically, and become more trustworthy over time, rather than creating content directly. The position starts with an R&D focus and expands from there, aiming to build a coherent and durable knowledge system from a partially-built foundation. This is a high-leverage, high-visibility role within the R&D Intelligence, Systems and Enablement (RISE) team, working directly with the VP of RISE and collaborating with Engineering, Product, and IO to make Upside's knowledge infrastructure a competitive advantage.

Requirements

Deep experience in knowledge management, information architecture, or technical writing with a strong systems orientation.
Demonstrated ability to design and implement governance frameworks.
Hands-on experience with Confluence and/or Glean (or equivalent enterprise knowledge and search platforms).
Comfort working across data, analytics, and tooling to instrument and measure knowledge health.
A "first-hire" mindset; thrives as a strategist, operator, and change agent.
Strong written communication skills to define what good looks like.
Experience with AI-assisted documentation workflows or LLM-based content tooling.

Nice To Haves

Background working in a technical environment (R&D, Engineering, or Product-adjacent).
Familiarity with analytics tools (Hex, Looker, or similar) for building dashboards.
Experience in an enablement or internal developer-relations type role.

Responsibilities

Build the Artifact Legibility Foundation for R&D by mapping current state, identifying gaps, and designing interventions (governance frameworks, lifecycle rules, ownership models, structural standards, AI agents).
Automate the Documentation Lifecycle by owning the design and rollout of AI-assisted documentation pipelines, integrating agents into the Product Development Lifecycle and release workflows, and setting up automated review triggers.
Solve the Routing Problem by replacing the current person-dependent request system with a well-governed intake pathway, enabled by an AI agent, and self-serve guidance.
Build Knowledge Observability by defining a metrics framework, instrumenting measurement infrastructure, and creating dashboards to visualize content health.
Drive the Shift from Service Model to Enablement Model by designing and running an enablement program with self-serve tooling, governance, team-level KPI views, and cultural change management.
Own Confluence and Glean as the primary business administrator, maintaining governance models and usage standards.
Maintain documentation standards, style guidance, and structural templates.
Define and manage lifecycle rules for content refreshment, archiving, and retirement.
Run content health audits and surface insights to leadership and team owners.
Serve as the internal expert on AI usage in documentation workflows and maintain guardrails.
Manage knowledge infrastructure transitions, including new team spaces, ownership migrations, and tool changes.