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.
Project Context
Chosen as Lead UX Designer on a tiger team tasked directly by CEO/leadership.
Mission: launch a new taxonomy system (CrowTaxo) to better integrate with Recruit, our parent company.
MVP: enhance job posting flow to capture richer qualifications/data, enabling better job matches.
My Role
As the Lead UX Designer on this initiative, 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 job posting flow enhancements
• Driving stakeholder alignment across multiple teams
• Delivering and finalizing design solutions
• Partnering with engineering teams to ensure smooth implementation and scalability
Discovery & Research
My research focus included:
• Mapping existing job posting flow and pain points.
• Reviewing past discovery/research on employer friction and data gaps.
• Conducting usability research on design iterations before engineering handoff for MVP.
• Mapping the existing job posting flow and pain points.
Key areas of focus uncovered:
• Integrating AI features (e.g., auto-generated job descriptions).
• Updating qualifications & segmentation pages for more flexible options.
• Removing friction/unnecessary steps in the posting flow.
• Partnering with jobseeker teams to ensure data/qualifications aligned on both sides.
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 Challenges & Explorations
1. Supporting More Inputs & Data
We needed to design a posting flow that could handle significantly more qualifications, job details, and segmentation inputs without overwhelming employers. The challenge was balancing data richness with usability and simplicity.
2. Iterating on Flows
Explored multiple flows for qualifications, job details, and segmentation, testing different approaches for layout, step grouping, and progressive disclosure. Iterations focused on reducing friction while making it easy for employers to provide richer job data.
3. Introducing Drag & Drop
To simplify input and re-ordering of job details, we introduced drag-and-drop functionality. This gave employers more control, reduced clicks, and created a smoother experience for managing multiple qualifications and requirements.
4. Updating the Chip Component
The increase in selectable qualifications and job details required clearer UI patterns. I updated the chip component in the design system to improve clarity, scalability, and accessibility. I also created a formal proposal and partnered with the design system team to drive adoption.
My contributions:
• Audited existing chip usage and identified gaps in clarity and accessibility.
• Proposed a new scalable design supporting both single-select and multi-select use cases.
• Improved visual hierarchy and states for better usability across desktop, mobile web, and app.
• Created a formal proposal and partnered with the design system team to drive adoption into the shared system.
Chip Component: Before
Chip Component: After Proposal
Single-select
Multi-select
5. Optimizing for Mobile Web & App
Since many employers post jobs on-the-go, I ensured the updated flows were fully optimized for both mobile web and native app experiences. This meant rethinking layouts, touch targets, and simplifying complex interactions like drag-and-drop for smaller screens.
6. Clarifying Required vs. Preferred Qualifications
Employers and internal teams were often confused by the distinction between required and preferred qualifications. This created complexity when reviewing candidates and even led to misalignment internally. I led additional research and discovery to validate how employers actually used these fields and how candidates perceived them. Based on these findings, we simplified the framework to reduce confusion and ensure qualifications were displayed consistently across the platform.
7. Iterations, Reviews & Alignment
This project went through countless design iterations, reviews, and presentations with product, engineering, and leadership. Each cycle helped refine the flow, resolve tradeoffs, and align stakeholders on the final MVP direction.
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
Final Designs
The MVP launch focused on simplifying the posting flow while laying the groundwork for better job–candidate matching.
Highlights:
Simplified job posting flow – streamlined navigation and reduced unnecessary steps.
AI-powered job descriptions – auto-generated text to speed up employer input while improving quality.
Qualifications taxonomy system – enabled more accurate matching through structured, scalable data.
Removal of “required vs. preferred” fields – reduced confusion for employers and ensured consistency when reviewing candidates.
Final Deliverable
MVP Deliverable
Impact Highlights
Onboarding & Signup
• Improved advertiser funnel with +2.7% lift in completion by simplifying entry points.
• Boosted ATS integration traffic by +94% and cut nav logging costs by ~$25K.
Job Posting UX
• Increased job posting completion rate by +1.94% through form simplification and cleaner UX.
• Achieved 99.9% uptime with backend and platform improvements.
Candidate Quality & Taxonomy
• Enhanced qualification engagement: +20% more quals per job, +36s time spent on page.
• Improved outcomes with +11% lift in hires using qualification-based filters.
Optimization & AI
Delivered 6+ AI experiments; highlights include:
• +21% engagement with auto-suggestion prompts.
• +12% more positive outcomes using contextual job recommendations.
Fixes & Foundations
• Resolved critical routing and logging issues affecting thousands of users.
• Migrated core systems (e.g., onboarding, screening, posting) to scalable platforms for future growth.
Reflection
Leading CrowTaxo was one of the most complex initiatives I’ve worked on, bringing together structured data and design to simplify a high-stakes flow. Collaborating across employer, jobseeker, and taxonomy teams reinforced the importance of cross-functional alignment when rethinking systems that impact millions of users.
Learned how to:
• Navigate leadership-driven initiatives with high visibility.
• Balance scalability, AI innovation, and usability.
• Push for design system updates while still hitting MVP deadlines.
• Set the stage for ongoing enhancements and future phases of CrowTaxo.
