AI Developer Advocate

What they do

They build small, working sample applications and notebooks that show how a tool is meant to be used. They write guides, run workshops and answer developer questions in forums and issue trackers. They watch where people get stuck, then record those patterns and share them with product and engineering. They test new features before release and say plainly when something is confusing. They also talk to developers about what they are trying to build, which shapes what the team makes next. Much of the week is code, writing and conversation in roughly equal parts.

What a strong hire delivers

Within the first months, a strong hire ships a small set of demos that run cleanly and that developers actually copy. They set up a regular way to collect developer feedback and turn it into short, specific notes the product team can act on. They fix or flag gaps in the documentation. They also build a visible, trusted presence in the developer community, so questions get answered quickly and honestly.

How you can tell they are good

Ask them to show a demo or tutorial they built and walk through the choices they made. Strong people explain who it was for, what confused learners, and what they changed afterwards. Ask for an example where their feedback changed a product decision. Be wary of candidates who talk only about reach and audience size, or whose code samples cannot be run by someone else.

Why it is hard to fill

The title varies between companies, so searches by title miss good people. The work needs real coding ability, clear teaching and comfort with public community work. Most candidates are strong in one or two of those, not all three. Engineers often dislike the writing and event load. Content people may lack depth in code. Employers also tend to want someone who already knows AI tooling, which narrows the pool further.

Where to find them

Look at developer advocates and DevRel engineers at developer tool companies, cloud platforms and API businesses. Software engineers who run open source communities or write widely used tutorials are good sources. So are technical writers who code, trainers and bootcamp instructors, and solutions engineers who enjoy teaching. Labs and AI platforms hire from all of these groups, so look across them.

Skills that matter

• Technical communicationAdvancedExplain evidence and limitations to relevant readers
• Change management and trainingAdvancedSupport adoption through learning and organisational change
• User researchWorkingStudy user needs, context and behaviour
• PythonWorkingDevelop tested numerical and data software