Prior Labs
AI
TechnicalWriter(PartTime)
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“Technical Writer (Part Time) at Prior Labs. Skills: technical writing, docs-as-code workflows, AI-assisted documentation workflow, Python. build our technical documentation practice from the ground up. improving and expanding what we have”
What You'll Achieve.
improving and expanding what we have; ensuring that our open-source repositories, API, and products are always well-documented; make it easy for a machine learning engineer to go from zero to productive with TabPFN in minutes, not hours; keep docs in sync with code changes; prioritise improvements based on user feedback and product changes
Industry & Context.
spot gaps before they become problems and propose solutions without being asked; Ability to independently research and synthesise technical concepts
What They're Looking For.
Must Have
3+ years of technical writing experience, ideally for developer tools, ML platforms, or open-source projects alongside engineering teams in a fast-moving startup or research environment, Hands-on experience with docs-as-code workflows: Markdown, Git, pull requests, and CI/CD-integrated documentation pipelines, technical writing fundamentals: clear structure, consistent terminology, audience-aware tone, Familiarity with AI-assisted writing tools and experience integrating them into documentation workflows, Possess university level Python to produce relevant example code to be used in live documentation, Ability to independently research and synthesise technical concepts from source code, papers, and conversations with engineers, Solid understanding of machine learning concepts, especially supervised and unsupervised learning, enough to write confidently for an ML-practitioner audience, Understanding of open-source contribution dynamics and how to write documentation that serves both core users and community contributors, You are proactive: you spot gaps before they become problems and propose solutions without being asked
Nice to Have
Prior experience setting up a documentation system from scratch, including style guides and contribution guidelines, Experience with video or visual content creation (screencasts, diagrams, interactive notebooks) as a complement to written docs
What You'll Do.
build our technical documentation practice from the ground up
improving and expanding what we have
establishing an AI-assisted docs process
ensuring that our open-source repositories
and products are always well-documented
make it easy for a machine learning engineer to go from zero to productive with TabPFN in minutes
and expand our existing documentation across our open-source repositories (TabPFN
tabpfn-client) and API reference
Author new content including tutorials
conceptual explainers
and real-world use case examples targeted for ML practitioners and data scientists
Design and implement a modern
AI-assisted documentation workflow using our current stack (GitHub
Mintlify) to keep docs in sync with code changes
Work closely with engineers to translate complex model behaviour and API design into clear
accurate developer-facing content
Contribute Mintlify-compatible Markdown that follows our style and renders correctly in our docs site
Own the docs roadmap and prioritise improvements based on user feedback and product changes
How You'll Work.
Team & Collaboration
Work closely with engineers to translate complex model behaviour and API design into clear, accurate developer-facing content
Communication Scope
clear structure; consistent terminology; audience-aware tone; clear, accurate developer-facing content
Process & Methodology
Own the docs roadmap and prioritise improvements based on user feedback and product changes
Full Job Description
WHO WE ARE Foundation models have transformed text and images, but structured data - the largest and most consequential data modality in the world - has remained untouched. Tables power every clinical trial, every financial model, every scientific experiment, every business decision. No one has built a foundation model that truly understands them. Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening - and we're hiring the team that makes it. Momentum: We pioneered tabular foundation models and are now the world-leading organization in structured data ML. Our TabPFN v2 model was published in Nature https://www.nature.com/articles/s41586-024-08328-6 and set a new state-of-the-art for tabular machine learning. Since its release, we've scaled model capabilities more than 20x, reached 3M+ downloads, 6,000+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics https://www.oxcan.org/news/prior-labs-and-oxford-cancer-analytics-partner-to-advance-liquid-biopsy-and-clinical-decision-making-in-lung-disease to preventing train failures with Hitachi https://siliconangle.com/2025/12/01/prior-labs-debuts-tabular-ai-foundation-model-scales-10-million-rows/ to improving clinical trial decisions with BostonGene https://priorlabs.ai/case-studies/boston-gene. The hardest work is in front of us. We're scaling tabular foundation models to handle millions of rows, thousands of features, real-time inference, and entirely new data modalities - while building the infrastructure to deploy them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level. Our team: We’re a small, highly selective team https://priorlabs.ai/about of 20+ engineers, researchers and GTM specialists, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G-Research, Ja
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