Uncommon
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.
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.
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.
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.
Early insights
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.
The Discovery
The Pain: Overwhelmed by Irrelevant Applications
Recruiters were drowning in a flood of unqualified resumes, spending hours sorting through noise just to find a few promising candidates.
Without smarter filtering, burnout was common, top talent was missed, and the hiring process dragged on.
Time spent sorting junk is time lost finding stars.
This reinforced a key takeaway for us: the design should focus on reducing noise and helping recruiters spend their time where it matters most, on real, high-potential talent.
Wasted Ad Spend
Employers spend thousands on job ads, but most clicks come from unqualified candidates.
Budgets drain without delivering quality hires, leading to high costs and low ROI.
Paying for clicks is useless if they don’t turn into real candidates.
This showed us that the value isn’t in driving traffic, it’s in helping employers connect with qualified, interested people. Our design should make that distinction clear and measurable.
Bias and Inconsistent Screening
Qualified candidates were often being rejected for coming from non-traditional backgrounds, race, or even gender.
This led to missed opportunities to hire diverse, high-potential talent, ultimately weakening team quality and limiting growth and innovation.
It was a classic case of judging a book by its cover, or trying to force puzzle pieces into the wrong places.
This made it clear that our design needed to surface deeper signals of merit, not just pattern matches, so overlooked candidates had a fairer shot and teams could uncover unexpected strengths.
Losing Top Talent to the Clock
Top candidates were getting scooped up by other companies before recruiters even had a chance to schedule interviews.
As a result, teams missed out on top talent, vacancies stayed open longer, and overall team quality suffered.
Speed is a competitive advantage in hiring.
This highlighted a key design priority: in hiring, even small delays can mean losing the best candidates. We needed to reduce friction, and look further upstream for opportunities to accelerate the search phase.
No Visibility, No Control
Employers struggled to understand which job ads were attracting quality candidates, making it nearly impossible to optimize their budgets.
Some platforms matched candidates based on what recruiters described as a "Black box", offering little more than keyword alignment and almost no insight into why someone was surfaced or how to adjust the results.
You can’t improve what you can’t see, or control.
This made it clear that transparency couldn’t be a nice-to-have. It had to be a core part of the experience, giving employers real feedback loops and tools to fine-tune their hiring strategies with confidence.
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?
User Journey
To synthesize the research, I mapped the full end-to-end hiring journey, capturing each phase of the traditional hiring process.
The journey made friction points easy to spot. Search and evaluation stood out as the most painful and time-consuming stages, frequently slowing down the entire process.
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
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.
Transformations
How we got here
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
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.
