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.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.