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2024
Designing LinkedIn’s AI Hiring Assistant
Hiring great talent is hard — especially for managers without a recruiting background. At LinkedIn, we set out to reimagine the hiring experience by designing an AI-powered assistant that helps managers write better job descriptions, source stronger candidates, and streamline the hiring process — all within LinkedIn Recruiter.
Project duration
6 months
My role
Product Design Lead
I led product design from initial vision through launch, working across design strategy, prototyping, research, and delivery.
Working team
3 product designers
3 product managers
6 engineers
1 researcher
1 content designer
& extensive collaboration with design systems, AI platforms, product marketing, data science and legal.
Project highlights
User problems
Hiring managers at LinkedIn — especially those at small and midsize businesses — were struggling with inefficient, inconsistent hiring processes. Writing job descriptions, sourcing qualified candidates, and evaluating applicants were time-consuming and intimidating for non-recruiters.
This problem was urgent because it led to delayed hiring, lower-quality talent matches, and poor candidate experiences — all of which hurt business outcomes and user trust.
Our goal was to create an AI-powered assistant that made hiring faster, simpler, and more effective, without requiring deep recruiting expertise.
The solution
We designed “Hiring Assistant,” a smart layer inside LinkedIn Recruiter that helps managers post jobs, discover talent, and evaluate candidates — all with help from AI.
The experience was rolled out in two phases: first, a lightweight 1-step posting and sourcing flow; then a more refined, customizable workflow based on beta feedback.
We began with a vision
In early workshops, our team generated dozens of ideas to reimagine the hiring journey with AI. We aligned around a vision that focused on simplicity, clarity, and confidence — empowering managers, not replacing them.
To gain stakeholder buy-in, we created high-level concept prototypes showing the end-to-end assistant experience.
Phase 1: MVP Experience
While a vision proposal is great to get buy in, after looking at timelines, business needs, development resources, we needed to scale back a few features so we developed a phased approach to get things built and delivered in a timely manner. We wanted to launch with a few key features.
1-Step Job Posting
AI generates a draft job post based on simple prompts (“What role are you hiring for?”). Managers can edit and adjust before publishing.
AI Sourcing
Candidates are recommended based on skill and experience match. Suggested outreach messages are also AI-generated.
AI Screening
As applications come in, Hiring Assistant evaluates and sorts top matches. Managers can save or reject in one click.
Friends & Family (closed beta) research and findings
During our “Friends & Family” closed beta, we gathered qualitative and quantitative feedback from high-value customers. These findings directly informed our Phase 2 design updates.
Phase 2: Feedback-Driven Redesign
User feedback revealed a strong desire for more control and transparency. We redesigned several parts of the experience:
Posting process
Replaced 1-click with a step-by-step flow so users could review and tweak each detail.
Optimizing the sourcing flow
Redesigned the candidate cards with better IA and richer info to improve scanning and decision-making.
Adjusted criteria to reduce false positives in the “Top Match” category.
Users noted our algorithm was putting too many people in the “Top match” category and it was not as easy to sort. We needed to make our requirements stricter.
Creating the style guide and integration with Design System + Guide teams
As we were developing this product, there were other agentic products being developed across the company. We were all working in tandem and eventually were all consolidated to work together in a new “agentic” design system. I helped develop this new design system to work across the company.
Project outcomes
My impact
As the lead designer, I drove alignment across a highly cross-functional team and helped scale the vision across multiple platforms. My early design concepts and systems work influenced not only Hiring Assistant, but also other AI products at LinkedIn.
Learnings and challenges
What did we learn?
Challenges we faced
Conclusion
Designing LinkedIn’s Hiring Assistant was a deep dive into the future of work — one where AI supports, not replaces, human decision-making. By listening closely to hiring managers, building in transparency, and refining based on real-world feedback, we created a tool that empowers people to make better hires — faster.
