• 2022–2025
  • XING
  • 20M+ members
  • AI search
  • Product systems
  • Award winner

Search, redesigned for AI

My role

I led the evolution of search over three years — from redesign to AI. I drove the redesign end-to-end through user research, then shaped the AI transformation by defining search strategy, key flows, prompt logic and system behaviour with engineering and data science.

Business / User need

Job search was slow, effortful, and overly dependent on exact keywords, with filters buried in deep hierarchies. We first turned search into a focused, easier-to-use product surface. Building on this, we introduced AI-assisted search to move beyond keywords — enabling more relevant results and stronger click-through.

Outcome and Impact
  • Click-through rates and application intent increased by over 18% — the strongest uplift I had seen in eight years at XING
  • Surfacing filters significantly increased interaction and engagement in early iterations
  • AI embeddings became a platform capability — replacing keyword-based search, powering logged-out search, and later driving significant uplifts in recommendations.
  • Recognised by Job Boards Connect for exceptional job seeker experience.

From redesign…

First search needed a strategy driven new interface
Frameworks don’t create impact — decisions do

What separates impact from cargo cult is knowing when to use which tool. We turned assumptions into hypotheses and prioritised uncertainty — focusing user testing where we knew least.

Uplift 20% How might we move users from networking to finding jobs?

We reclaimed space from cross-site navigation and gave it to the filters users needed most. The filter bar stayed reachable while scrolling, made active selections visible, and helped users narrow large result sets without breaking their search flow. On iOS, filter usage increased by 20% — an important step towards more relevant results and stronger click-through.

Figma specification board for AI-assisted search flows and interaction patterns
Problem: Search competed with everything

The old page treated search as one element among many. Navigation, branding and secondary actions pulled attention away, while filters were hidden behind extra clicks or unclear labels. Users rarely discovered the ways they could narrow thousands of results.

Figma specification board for AI-assisted search flows and interaction patterns
Uplift 22.9% Solution: Turn search into a focused funnel

We gave search visual priority, flattened the filter hierarchy and surfaced meaningful choices such as Remote and work model directly in the flow. Qualitative testing showed that visible filters inspired users to refine their search. Quantitative data then showed filter usage rising by 22.9%.

…to intelligence

How do we transform this interface into an AI embedded search?
Analytics graph showing sustained click-through uplift for AI search compared with old search
Spoiler alert: The shift to AI search worked

The move from keyword search to AI-driven discovery created a clear, sustained uplift — validating both the redesign foundation and the new search paradigm.

Sketch illustrating the shift from keyword-based search to AI-driven search, using trains that are on rails for keyword search and a train that has wings and can fly in any direction for AI search
Detailed hand-drawn sketch of an “everything search” interface, mapping filters, results, and AI-assisted features
We set out to move job search from keywords to intent.

AI didn’t start with technology — it started with a question: how do people actually describe the job they want?

How do we help users express intent instead of guessing the right keywords?

One of our problems we needed to solve
Reference UI screen
Problem: Tooltips get dismissed

Small input fields still suggested short keyword searches. But AI search needed users to write richer, intent-driven queries. Tooltips were easy to dismiss before the guidance landed.

Interactive CodePen prototype
Interactive! Solution: Help where you type

We turned the field into a larger writing area and replaced tooltips with animated placeholder prompts. The guidance appeared at the moment of use, could evolve across repeated visits, and let us surface a wider range of hints without interrupting the search flow. The result: at least 20% more users wrote longer queries.

The bigger input field made me write a full prompt instead of keywords.

User interview, 2024
Problem: Writing is work

We encouraged users to write more detailed, intent-driven search prompts. But creating a good prompt took effort — and that friction meant many users simply did not do it.

8% uplift! Solution: Generate the prompt

For logged-in users, we already had enough profile data to create a strong first prompt for them. Search with my profile turned that information into a ready-to-use query, increasing click-through and application rates by up to 8%.

Exploratory AI search interface directions in Figma
Catching the AI wave early

As AI and embedding-based search began turning from promise into product reality, we explored how the interface could evolve before the final system existed. Using internal prototypes and early embedding technology, we tested ways for users to move beyond keywords — with many patterns later shaping the shipped product.