A lot of job search still depends on people being able to clearly state what they want. But career intent is often messy. People may be open to adjacent roles. They may be unsure about salary, seniority, industry, flexibility, or timing. They may be actively looking, quietly monitoring, or just trying to understand what is possible.
The experience I worked on used conversation as a way to capture that intent more naturally.
Not open-ended chat for the sake of it. Not a novelty layer on top of search. More like a light, structured exchange that helps the product ask the right question at the right time, learn from the answer, and improve what the person sees next.
The aim was to make recommendations feel more useful, more transparent, and more in tune with where someone is in their career. That meant thinking carefully about when to ask, what to ask, how much effort to ask for, and how each answer could lead to a better feed, better job matches, or better career context.
I’ll share more soon on the design thinking behind it, including:
- how conversational AI can help people express career intent
- why the best question is often more useful than a big form
- how to balance user control with smart personalisation
- what it means to design AI that earns trust, rather than asks for it.
I won’t be sharing confidential product details, internal strategy, or implementation specifics. But I will share the design problems, the trade-offs, and the lessons that shaped the work.
Coming soon.