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 saw an opportunity to solve this with AI, using machine learning to surface the most qualified talent, backed by data.
I joined an ambitious project to reimagine the hiring experience in the HR tech space by building a smarter, faster, and more accurate way to connect great candidates with the right roles.
Why Traditional Recruitment is a Sinking Ship
By 2018, the average corporate job posting received over 250 applications, but only about 2% of applicants made it to the interview stage.
Recruiters were drowning in irrelevant resumes, overspending on job ads, and losing top talent to slow, biased processes. The system was broken.
Hiring felt like fishing with a torn net. You cast it wide and pay for reach, but end up sifting through noise while the best candidates slip away.
My Role
From June 2017 to February 2019, I led the end-to-end user experience design in close partnership with a Product Manager.
We launched in February 2018, and I continued supporting the product for a year post-launch before moving on to my next chapter.
The Challenge
Earning Trust in AI Hiring
One of our biggest hurdles was overcoming skepticism around using AI in the hiring process. Recruiters were cautious, and rightfully so. Trust had to be earned.
Our initial goals were:
Prove that AI could surface better, data-backed matches
Speed up hiring by identifying qualified candidates faster
Reduce bias by evaluating people base on merits, not just where they’ve been
This meant designing not just for efficiency, but for transparency and confidence at every step.
Kickoff
Before I joined, the team had built the experience without a designer. The result was low conversion rates and high user drop-off.
I began by suggesting a few quick fixes, but as I dug deeper and got to know our users, it became clear that the real problems ran deeper. What looked like surface-level friction was rooted in a lack of user-centered thinking.
Discover
Unpacking Hiring Myths
We interviewed recruiters and hiring managers from both client teams and our own. A few common misconceptions kept showing up:
“More applications mean better candidates.”
In reality, more volume often creates more noise, not better results.“Keyword matching guarantees qualified candidates.”
Great talent is often overlooked when they use different language to describe their skills.“Top schools or big companies mean top talent.”
Strong candidates can come from anywhere, not just prestige-filled resumes.“You need to spend heavily on ads to get results.”
Most job ads generate quantity over quality, flooding recruiters with irrelevant resumes.
These early insights gave us a strong foundation to reframe the product around what actually drives better hires, not just what recruiters were used to.
A Pipeline Full of Noise
In a majority of interviews, one theme came up again and again: recruiters were overwhelmed. Their pipelines were full, but not with the right candidates. They were spending hours sifting through resumes, only to find that most were unqualified or irrelevant.
This high volume of noise created real consequences. Promising candidates were missed. Hiring cycles dragged on. And worst of all, recruiters felt drained and discouraged, stuck in reactive workflows that left little room for strategy or relationship-building.
What we uncovered wasn’t just a workflow issue, it was a deeper emotional strain. Time that could have gone toward meaningful connections was being lost to low-value tasks, leading to fatigue and, in some cases, burnout. It became clear that volume alone wasn’t the challenge, the lack of prioritization and clarity was taking a real human toll.
High Spend, Low Return
Employers were spending thousands on job ads, yet most of the traffic came from unqualified or disengaged candidates. Clicks were high, but conversions to real applications, or quality hires, were disproportionately low.
This created a painful disconnect between spend and value. Hiring teams watched budgets disappear without clear returns, often feeling misled or powerless to control the outcome. The deeper we looked, the more evident it became that volume alone wasn’t the problem. The issue was misalignment, ads were optimized for traffic, not for relevance or intent.
This insight reframed how we approached the challenge. What employers really needed wasn’t just more exposure, it was a reliable way to attract qualified, motivated candidates. And what they lacked was visibility and control over the quality behind each click.
Bias in the Pipeline
Qualified candidates were being filtered out based on surface-level traits, non-traditional career paths, race, gender, or lack of brand-name credentials. These quick judgments were often unconscious, but deeply embedded in how decisions were made.
The result was costly. Teams missed out on high-potential talent who brought fresh perspectives, diverse experiences, and long-term value. Over time, these patterns narrowed the hiring lens, weakened team diversity, and limited opportunities for innovation and growth.
Behind the metrics, there was a human cost too. Many candidates never had the chance to show what they could bring to the table. Recruiters expressed frustration that gut instincts and pattern-matching often overpowered thoughtful evaluation, especially under pressure to hire fast.
These insights made it clear: the problem wasn’t just bias, it was a lack of meaningful, merit-based signals that could help teams see beyond the obvious.
The Cost of Hiring Too Late
Competition was fierce, and high-quality talent moved fast. Delays in scheduling, internal reviews, or even sourcing meant that companies were consistently missing their best opportunities.
This had a ripple effect. Roles stayed unfilled longer, team capacity suffered, and recruiters expressed growing frustration at seeing strong prospects vanish before they could act. There was a sense of always being a step behind.
The problem wasn’t just slow tools, it was a fragmented process.
Bottlenecks were hidden early in the pipeline, and there were few mechanisms to prioritize high-interest, high-fit candidates in real time.
The emotional toll was clear, missed talent wasn’t just a metric, it felt like losing a race no one realized they were running.
Define
Problem Statement
Recruiters and hiring managers struggle to find high-quality talent, drowning in irrelevant applications, wasting money on ineffective ads, and missing top candidates due to slow, biased processes. Without clear insights, they waste time and make poor hiring decisions.
How might we make it faster and more cost-effective for recruiters to discover top talent, without bias, and with evidence they can trust?
Design Principles
Here are a few guiding principles that helped us stay focused, prioritize the right features, and deliver a cohesive user experience:
Signal Over Noise
Prioritize candidate quality, not quantity. Recruiters are overwhelmed by keyword-matching systems that surfaced too much irrelevant data. We must focus on surfacing meaningful patterns over superficial matches.
Trust Through Transparency
Recruiters are naturally skeptical of black-box algorithms. We will provide clear, explainable logic and evidence in order to build trust, boost adoption, and gives users confidence in the results.
Design for Action at Scale
Recruiters want to move fast and at scale to stay competitive. The experience needed to support rapid, high-volume decision-making without friction.
Design for Fairness
AI systems can easily reinforce existing biases if left unchecked. We must design with fairness in mind, proactively reducing bias wherever possible to support more equitable hiring decisions.
Design
Designing for Signal, Not Volume
Traditional keyword matching overwhelmed recruiters noise: hundreds of irrelevant resumes they had to manually reject per job. It was clear the problem wasn’t volume. It was quality.
To explain our shift in mindset, we often used the haystack analogy: the best way to find a needle isn’t to keep digging—it’s to remove the hay (noise) and bring in a magnet (signal).
This became a guiding mental model in how we approached matching: reduce noise and increase signals.
Build Trust Through Transparency
Recruiters felt skeptical about letting AI influence hiring decisions, especially when the logic behind matches felt opaque.
We often heard recruiters describe AI-powered platforms as a “black box,” offering little explanation and no way to adjust the results. So we took the opposite approach: we surfaced clear, verifiable justifications for every match directly within the candidate profile.
Each match's profile included a side-by-side comparison between the role’s criteria, the matched criteria, and the parsed resume, laid out in a clean, consistent format that made it easy to scan, understand, and validate.
All justifications were tied to the employer’s own input, making them both relevant and contextual.
By showing exactly how and why candidates were matched, we gave recruiters confidence in the results, without requiring them to trust the system blindly.
Full Control, Without Bias
Recruiters wanted more control over their searches, but with flexibility often came the risk of reinforcing bias.
Our approach was to give recruiters meaningful control over search, allowing them to include or exclude up to 16 types of qualifications and see results update instantly. This made it easy to fine-tune matches based on what actually matters.
At the same time, we intentionally removed bias-prone criteria like school, gender, and race, attributes shown to reinforce unconscious bias.
The result was a search experience optimized for merit-based decisions, empowering recruiters to focus on skills and potential, without compromising fairness.
Prioritizing Passive Talent: Giving Recruiters A Head Start
Relying solely on active applicants meant missing out on top-tier passive candidates, people who rarely apply directly but are often the best fit for the role.
To address this, we prioritized showing passive candidate matches at the beginning of the experience.
Instead of waiting for applicants to come in, recruiters could instantly view matched profiles of high-potential, passive candidates. When they chose to move forward, our Automated Outreach handled the initial engagement, making it easy to spark interest with minimum manual effort.
This approach expanded the talent pool beyond active seekers and shifted the hiring experience from reactive to proactive.
Bulk Efficiency: Take Action at Scale
Recruiters are always thinking in bulk. With constant time pressure and hundreds of candidates to manage, even a few seconds saved per action can translate into meaningful leverage.
To support this, I experimented with a prominent, customizable CTA designed specifically for batch actions. By making bulk action the default interaction, we intentionally nudged recruiters to shift from reviewing candidates one by one to acting at scale.
This wasn’t just about speed. Recruiters won’t engage in bulk unless they trust the system to deliver quality. So when they do, it’s a strong signal of confidence in the platform’s matching ability.
By framing bulk actions as the expected flow, we reinforced efficiency, encouraged trust, and created a more scalable, repeatable experience for users who come back again and again.
Deliver
Introducing: Uncommon.co
In a world where finding candidate-fit is a costly rarity, Uncommon is a programmatic recruiting marketplace with human-interpretable matching mechanics that accurately matches recruiters with 100% interested and qualified candidates, to help recruiters shorten time-to-fill and reduce cost-per-hire.
Product Demo
(Need a better view? Click the expand icon in the top right, then hit "Z" to zoom it just right.)
The launch
In just 8 months, we transformed the platform from a simple job site into a fully reimagined, programmatic, AI-powered recruiting experience.
It became a rare win-win-win:
Recruiters could find more qualified candidates, faster and without bias.
Candidates got a fairer shot at great roles, no longer overlooked due to rigid filters.
And we delivered real value through thoughtful, responsible design.
Uncommon.co officially launched in February 15, 2018
We raised $18 million in funding to support its operations and growth initiatives.
