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San Francisco, California, United States
Onsite
Alembic is the pioneering Causal AI platform. We help the world's largest enterprises move past correlation to prove what actually drives business outcomes — the question marketing and growth teams have never been able to answer with confidence. Fortune 100 companies including Nvidia, Delta Air Lines, and Mars use Alembic to make multimillion-dollar decisions on trusted, causal evidence.
We're backed by a $145M Series B from WndrCo (founded by Jeffrey Katzenberg), Jensen Huang, Joe Montana, Prysm Capital, and Accenture. Our models run on our own NVIDIA DGX SuperPOD built on Grace Blackwell infrastructure — one of the fastest private supercomputers in the world. (We've melted GPUs getting here.)
We're hiring a Technical Writer to build the single source of truth for Alembic's science. Our founder and CEO, the inventor behind our core technology, and our Chief Science Officer have produced years of research notes, testing and verification programs, and technical analyses. That work is spread across dozens of documents, and the links between them mostly live in their heads. You'll organize it into a verified, connected knowledge base the whole company can query and trust, run the internal peer review that every external technical claim passes through, and turn the strongest material into white papers and a technical blog.
We'll measure success by how much easier it becomes to find and trust what we know: fewer documents, each one canonical, sourced, and current. This role is onsite in San Francisco and reports to our Chief Science Officer, working day to day with our CEO and closely with Product, Engineering, and Customer Success.
Inventory our existing research and technical documents, map how they relate, and decide what is canonical, what is superseded, and what is missing.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.
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.
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.
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.
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.