Overview
New AI technologies like vector search gave us a chance to rethink how we organize and match jobs with jobseekers. We replaced our old taxonomy with a system that better reflects how people actually understand job roles. This improved both the accuracy of job matches and made job posting simpler and more intuitive for employers.
The Problem
Indeed's core mission is to help people get hired, which we have traditionally done by matching people to jobs. For many years, we relied solely on job titles or raw job description text. However, this approach lacked precision and depth.
We built a taxonomy system that was used throughout the site—for search filters, job listings, and the job posting flow. But as matching technology evolved, the system needed to be updated to support more personalized and accurate experiences for both employers and jobseekers.
Goals
This project aimed to:
- Deepen our understanding of jobs and jobseekers.
- Foster stronger emotional investment from jobseekers by better reflecting functional labor through job content.
- Improve accuracy in job classification and matching.
- Reduce taxonomy misclassification and support tickets.
- Increase completion rate and satisfaction with the job posting flow.
- Align taxonomy across systems for consistency.
My Role
I was the lead designer on this project, focused on the Employer Platform. My responsibilities included:
- Conducting research and mapping the existing job posting flow
- Collaborating with PMs, data scientists, and taxonomy experts
- Partnering with jobseeker design teams to ensure cross-platform alignment
- Leading design and iteration of the new taxonomy interface and posting flow enhancements
- Driving stakeholder alignment across multiple teams
- Delivering and finalizing design solutions
Process
- Deep dive into current and new taxonomy structures
- Research: employer pain points, usability issues, internal feedback
- UX audit of existing posting experience
- Close collaboration with jobseeker design and taxonomy teams
- Early concept sketches and iterations
- Prototypes and usability testing with employers
- Developer collaboration to refine and finalize implementation details
Design Solutions and Outcomes
We updated the job posting flow to align with the new taxonomy and improve usability through AI and structured input. Key improvements included:
Taxonomy Integration & Input Improvements
- Redesigned the taxonomy input with guided logic and contextual suggestions
- Enhanced the qualifications section to support rich, structured data and added a drag-and-drop feature
- Unified taxonomy implementation across related experiences and systems
AI-Driven Enhancements
- Integrated AI to auto-generate job descriptions and speed up the posting process
- Used AI to recommend relevant qualifications based on employer input
Flow & Usability Enhancements
- Improved navigation and step-by-step clarity throughout the job posting flow
- Updated design system components for better usability and consistency
Results
Reflection
This project was a meaningful opportunity to explore how structured data and thoughtful design can come together to simplify a complex, high-stakes experience. Working across both jobseeker and employer teams deepened my understanding of the challenges on each side of the hiring journey—and reinforced the importance of building systems that support both with clarity and empathy.
One of the biggest challenges was understanding how the existing system functioned and identifying the right path forward for improvement. Collaborating closely with taxonomy experts was especially rewarding; it highlighted the value of cross-functional alignment when rethinking foundational systems that impact millions of users.