Overview
Advancements in matching technologies like embeddings and vector search gave us a unique opportunity to rethink our current taxonomy system and replace it with a new system that would add more nuance and accuracy in how we understand and connect jobs with jobseekers—closely aligning with how recruiters and jobseekers interpret roles. This also allowed us to enhance the job posting experience, making it easier and more intuitive for employers while leveraging AI to deliver simplified tools for improved usability.
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.
Through the scalable segmentation initiative, this taxonomy was embedded across the user experience: powering matching, filters, job cards, and the job posting funnel. But the system needed to evolve to support modern matching techniques and create more personalized, accurate experiences for both employers and jobseekers.
Goals
Greatly increase 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, specifically for Crowtaxo on the Employer Platform.
My responsibilities included:
Conducting research and mapping the existing posting flow.
Collaborating with PMs, data scientists, and taxonomy experts.
Partnering with jobseeker design teams to ensure cross-platform alignment.
Leading the design and iteration of the new taxonomy interface and enhancements to the job posting flow.
Driving stakeholder alignment across multiple teams.
Delivering and finalizing designs.
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
Redesigned taxonomy input with guided logic and contextual suggestions
Updated the job posting flow to reflect the new taxonomy structure
Enhanced the qualifications input section to help employers provide rich, structured data and added a drag and drop feature
Implemented AI technology to speed up the process of the job description within the job posting flow
Improved navigation and step-by-step clarity throughout the flow
Unified taxonomy implementation across related experiences and systems
Updated components within the design system for usability
Reflection
This project allowed me to explore how structured data and UX can come together to improve complex enterprise flows. Working across jobseeker and employer teams provided valuable insights into creating a unified taxonomy that serves both sides of the hiring equation. The collaboration with taxonomy experts was especially rewarding and highlighted the importance of cross-functional alignment when tackling foundational systems.