WHAT YOU'LL DO
Inventory our existing research and technical documents, map how they relate, and decide what is canonical, what is superseded, and what is missing.
Build and maintain Alembic's internal knowledge base in Google Workspace, including an AI layer (Gemini and NotebookLM) grounded in curated sources, so answers come back with citations to the documents behind them.
Run internal peer review for external technical content and LLM-assisted drafts: trace each claim to a source, flag what can't be traced, and route open questions to the right scientist.
Assess the external research we build on: summarize each paper's claims, known critiques, and open debates, and how our position holds up against them.
Launch and run Alembic's technical publishing, including blog posts, white papers, and articles drawn from existing material and cleared through review.
Extend the knowledge base into customer-facing documentation and training materials with Product and Customer Success.
WHAT WILL HELP YOU SUCCEED
4+ years writing or editing technical or scientific material professionally, ideally in AI, data, research, or another field where accuracy gets checked.
Quantitative literacy: you can read a machine learning, statistics, or physics paper, explain its assumptions, and say where it is weakest.
Experience building a knowledge base, wiki, or documentation system out of scattered material, including taxonomy, naming, and upkeep.
Fluency with LLM tools, paired with the discipline to verify every claim they produce against a source.
The confidence to question a founder or scientist when a claim isn't supported, and the tact to keep that conversation productive.
Experience with an editorial or peer-review process (journal, research organization, medical or regulatory writing, or a docs team with formal review).
Published technical writing such as white papers, technical blog posts, or long-form articles.
Comfort with diagrams, process flows, Markdown, and docs-as-code tools; Python or scripting is a plus.
Familiarity with causal inference, experimentation, or marketing measurement is a plus.
THE ROLE IS RIGHT FOR YOU IF
You've taken a scientist's scattered work and made it usable by an entire company, and you want to do that alongside the inventor of the technology.
You care more about whether a sentence is true than about how many sentences you shipped this week.
WHY YOU MIGHT BE EXCITED ABOUT ALEMBIC
In the room with the inventor: You'll work directly with our founder and CEO and our Chief Science Officer on next-generation attribution models grounded in mathematical physics.
Build the foundation: You'll define how Alembic stores, reviews, and publishes what it knows, from the knowledge architecture and review standard to the technical blog.
A science story with proof behind it: Causal AI running on our own NVIDIA DGX SuperPOD on Grace Blackwell infrastructure, one of the fastest private supercomputers in the world.
Accuracy that matters: Fortune 100 companies including Nvidia, Delta Air Lines, and Mars make multimillion-dollar decisions on this work.
Series B momentum, real ownership: Meaningful equity at a Series B company that's raised $145M, with proven product-market fit and Fortune 100 traction.
WHY YOU MIGHT NOT BE EXCITED
You measure your output in pages published. Here, merging three overlapping documents into one may be your best work of the week.
You want a finished style guide, toolchain, and review process on day one. You'll build them.
You prefer writing from finished specs to sitting with a scientist, interrogating rough notes, and pushing back when a claim isn't supported.
