Uncommon: AI Recruiting · Talent Marketplace
Intro
Recruitment in the Age of Mass Applications
By 2017, job boards like Indeed and LinkedIn had made it easy to apply to dozens of jobs with a few clicks. The result? Recruiters were overwhelmed with low-quality applications, making it harder to identify top candidates.
Uncommon was a job board startup struggling to help candidates find success. I initially joined the team to improve conversion and overall usability.
Over time, we pivoted the product and reimagined the hiring experience for recruiters, focusing on building a smarter, faster, and more accurate way to connect great candidates with the right roles.
My Role
From June 2017 to February 2019, I led design as the only designer on the team, working closely with data scientists to improve our matching capabilities, and collaborating with the PM on user interviews and product strategy. We launched the product in February 2018.
Problem
Systemic Friction in Modern Recruiting
Recruiters and hiring managers struggle to find high quality candidates. They spend money on ineffective ads, drown in irrelevant applications and miss top candidates due to slow, biased processes. Without a better system, they waste time and money, risk making poor hiring decisions.
Key Frictions in the Current Experience:
Managing multiple sourcing platforms slows hiring momentum
One-click applications flood teams with unqualified candidates
High quality candidates are missed due to skimming and rigid keyword-based filtering
Bias creeps in as teams rely on learned shortcuts under pressure
Iteration
Designing for Match Quality, Not Volume
I designed the experience around the JTBD of reviewing and validating matches, where recruiters spend the most time and effort.
Rather than optimizing for applicant volume, we focused on matching roles with highly qualified passive candidates, enabling near instant matches without waiting for applications.
The experience intentionally slows recruiters down at the moment of evaluation to improve decision quality and feedback.
This was achieved by:
Removing traditional filters to make results feel effortless
Showing one candidate at a time to encourage focused review
Requiring explicit feedback on rejections to improve future matches
Putting Recruiters Back in Control
Initial testing showed that while match quality was strong, some recruiters wanted more control to refine roles that had nuanced requirements, specific constraints, or when recruiters needed to sanity check the system before moving forward.
I designed the criteria page to balance flexibility with guidance, without reverting to rigid, traditional controls.
Key considerations included:
Placing criteria on a separate settings page to keep the main results experience feeling effortless and AI led
Making filter criteria editable and flexible rather than fixed
Using natural language to reduce intimidation around the AI matching process
Providing visual feedback to guide tuning, since both overly narrow and overly broad criteria led to poorer matches
Deeper Insight
The Unexpected Truth, Recruiters Felt Sidelined
Recruiters felt sidelined, as if they no longer play a crucial role.
Instead of just handing everything over to AI, they want to feel needed and in control. They want to bring value to their work by adding nuance to the criteria and personally verifying each match.
What we discovered was that the goal isn't to eliminate the recruiter’s efforts. It's about giving them the tools to become even more effective, empowering them and making them the heroes of the hiring process.
Final Design
Familiar, Yet Supercharged
Driven by this emotional insight, I revamped the experience to help recruiters feel confident and in control. I placed criteria filters alongside the results to restore familiarity and allow recruiters to see matches update in real time, creating a tighter feedback loop as they refined their criteria. I also evolved the design system to feel more professional and utilitarian, optimizing the interface for focused, high quality decision making.
Key design updates included:
Clean, structured layouts with generous white space to reduce cognitive load
Familiar ATS and CRM patterns that help recruiters feel immediately oriented
Subtle use of color, badges, and tags to support fast scanning without distraction
To further reinforce ownership and control, I introduced recruiter specific workflows such as:
Shortlisting
Status tracking
Bulk actions
To give recruiters full agency and control over nuance, we introduced an extensive filter system with 16 criteria types. Each criterion could be duplicated, allowing one to include while another excluded.
Easily Verifiable Evidence
To help recruiters confirm the match and feel confident before moving forward, I added a candidate details modal with the following goals:
Clearly show which criteria were met and why
Present every resume in a consistent, easy to read format by parsing and restructuring content, regardless of original layout
Result
Introducing: Uncommon.co
In just eight months, we transformed Uncommon from a simple job board into a fully reimagined, AI powered recruiting experience.
The result was a rare win for everyone involved:
Recruiters found more qualified candidates faster, with reduced bias
Candidates received a fairer shot at meaningful roles, no longer filtered out by rigid criteria
The platform delivered real value through thoughtful, responsible design
Uncommon officially launched on February 15, 2018. Within months:
Over 200 clients joined the platform
Customers averaged more than 8 hires per month
The company raised $18 million to support continued growth and operations
Takeaway
Surface level complaints rarely tell the full story. Unspoken emotional needs influence behavior beneath the visible pain. We must keep testing beyond the initial solution to uncover what truly gives meaning to the experience.
