Knowledge Architect

As an IC3 (Practitioner) Knowledge Architect, you will add dedicated documentation and knowledge infrastructure capacity to our team. You will write and maintain internal technical documentation for Product and Engineering teams, including systems documentation, workflows, runbooks, and guides. You will build and maintain the onboarding content new engineers rely on in their first weeks.

You will work directly with engineers and subject matter experts to capture knowledge that today exists only in people’s heads, and you will help maintain the CMS structure, navigation, and content lifecycle that makes it findable. You will work to the standards set by the Staff Knowledge Architect and take requests through our intake process from scoping to publication.

• Write, edit, and maintain internal technical documentation for Product and Engineering teams, including systems documentation, workflows, runbooks, and guides.
• Build and maintain onboarding and informational content for department and team spaces, and backfill coverage for teams not yet served.
• Partner with engineering teams and subject matter experts to capture knowledge that is currently undocumented or held informally.
• Maintain knowledge assets in Confluence, including space structure, navigation, labeling, and content lifecycle.
• Support the AI Developer Tools Hub and other KA-maintained hubs.
• Take documentation requests through intake, scoping, delivery, and publication, and communicate scope, timelines, and status to requesting teams.
• Run recurring audits against KA’s published estate for accuracy, coverage, and discoverability, and remediate the gaps you find.

Experience:

• Auditing an existing documentation estate and remediating gaps in accuracy, coverage, or discoverability.
• Applying a documentation style guide and template set consistently, and using AI tools to scale and improve your output.
• Interviewing engineers or subject matter experts and converting what you learn into published documentation.
• Writing and maintaining technical documentation for a technical audience, such as systems documentation, runbooks, workflows, or developer guides.
• Managing a request queue in Jira or a comparable system, including scoping work and committing to delivery dates.
• Prioritising and delivering across multiple concurrent stakeholder teams.
• Owning content in Confluence or a comparable platform, including page structure, navigation, labeling, and templates.
• Experience with enterprise search and content discoverability, including how structure and metadata affect retrieval.
• Experience with docs-as-code workflows.
• Writing for both the platform engineers who operate a system and the researchers or customers who consume it; for example, runbooks and user-facing documentation for the same product.
• Documenting HPC or ML workload schedulers such as Slurm, Slurm on Kubernetes (SUNK), or Kueue, including day-2 operations such as upgrades, node drain states, and control plane troubleshooting.
• Familiarity with the inference and training stack that ML platform teams build on, such as vLLM, SGLang, TensorRT-LLM, or NCCL, and with how benchmarks such as MLPerf are run and reported.
• Defining information architecture for a large or fragmented content estate.
• Documenting cloud, Kubernetes, GPU, and AI and ML infrastructure.
• Building or maintaining onboarding content for engineering organizations.

We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams, even if you aren’t a 100% skill or experience match. You like working with a small, collaborative, tight-knit team. You’re curious about how things work, and you ask the question the engineer assumed everyone already knew the answer to. You’re an expert at structuring information so people find it without being told where it lives. You love turning an undocumented system into a page someone can actually follow. You’d rather fix the root cause of a recurring question than answer it again.