AI Technical Writer Jobs
Document the Future.

You don't need to train models or write production ML code. But you do need enough AI literacy to understand what you're documenting. This means grasping concepts like embeddings, tokens, inference, fine-tuning, and common architectures. You should be comfortable reading Python code, running scripts, and testing APIs. Think 'technically curious generalist' rather than 'ML engineer who writes.'

Yes, code examples are essential. Most AI documentation includes Python snippets, API calls, and integration code. You'll need to write, test, and maintain these examples. The good news: you're not building production systems. You're writing clear, well-commented code that demonstrates concepts. Strong Python fundamentals and the ability to work with REST APIs will cover 90% of what you need.

Start by building AI literacy through hands-on learning. Take the fast.ai course, experiment with OpenAI and Anthropic APIs, and read existing AI documentation critically. Then, contribute to open-source AI projects that need docs. Many projects have documentation gaps you can fill. Document your own AI learning projects. This portfolio proves you can explain AI concepts clearly.

Include 3-5 pieces showing range: an API quickstart guide, a conceptual explainer (like 'How embeddings work'), a tutorial walking through a real integration, and code samples with clear comments. Bonus points for contributing to open-source AI projects. Show you understand both the 'what' and the 'why' of AI documentation.

Many are fully remote or hybrid. AI companies often have distributed engineering teams, so documentation work follows suit. Companies like Anthropic, Hugging Face, and many startups hire remote technical writers globally. That said, some roles at larger companies (Google, Meta) may prefer or require office presence.

Growth paths include: Senior Technical Writer (deeper specialization, complex projects), Lead/Staff Writer (documentation strategy, mentoring), Documentation Manager (team leadership), Developer Advocate (more external-facing), or Product roles (leveraging your deep product knowledge). Some writers transition into AI engineering or product management as they build technical depth.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.