#StartupsEverywhere Profile: Laura Truncellito, Founder, Enployable
This profile is part of #StartupsEverywhere, an ongoing series highlighting startup leaders in ecosystems across the country. This interview has been edited for length, content, and clarity.
Developing AI Systems to Ease Job Searches & Maximize Career Happiness
Enployable is a new AI-powered platform that matches job openings with potential candidates through systems that identify cultural and personal priorities for all parties involved. Based in Tysons, Virginia, Founder and CEO Laura Truncellito walked us through the company’s platform and goals, the importance of building equitable artificial intelligence systems, and how she thinks policymakers can support AI startups.
Tell us about your background. What led you to Enployable?
My background is in machine learning, which is a specific technique around artificial intelligence (AI) that uses data to seek predictions and enhance the intelligence of machines. I like to say AI is made by humans, for humans. I'm passionate about learning as a person myself and making machines smarter. I do believe that, ultimately, AI benefits mankind and that's why I enjoy building these types of systems. That’s what led me to build Enployable as an AI-based startup. We use AI to help people find good jobs and we enable that process by focusing on what people care about.
We ask our users about their work style, beliefs, and life goals. We want to tie those components of who you are into your job applications. On the employer side, we operate in a similar manner. We want to help employers find employees that are aligned with their mission and culture. Our belief is that this alignment is critical to enhancing the overall happiness of employees and increasing retention. With the Great Resignation, approximately 74 percent of the workforce are thinking about quitting this year. We have the opportunity to help employers ensure they’re finding new employees who are the right fit for the role and their company.
What is the work you all are doing at Enployable? What is the experience of using your platform?
Any job candidates can come in to find jobs at companies that fit their work and lifestyle for free. We collect information on peoples' past and present experiences with certain companies and utilize that data to determine who will best match these companies and roles. Our matching techniques are based on factors that users identify as important to them. For example, you could have a similar job opening across a group of tech companies, but they may not necessarily have the same culture. Our model allows for you to grasp the cultural scopes of these workplaces ahead of any potential employment so you would know the likely best fit.
On the employer side, we have a similar offering that’s operating on a different AI engine. That platform allows companies to find prospective employees that reflect their same values and interests. Most of the employers we have on that platform right now are startups, actually. Startups and early-stage companies need to hire quickly, but they also have a unique culture and they need talent that will fit with who they are. We also have onboarding and ongoing engagement plans for employers since talent acquisition is just phase one. These plans help employers navigate the hiring process based on their missions and culture.
Are there ways that policymakers could better support startups like yours that build, use, and rely on emerging technologies like AI? Are there other considerations policymakers need to keep in mind when thinking about these technologies?
I think when people talk about AI, they’re unaware that it actually has applications in a variety of different areas. For example, when people think about AI, maybe they think about robots or things you see on Star Trek. And I think for policymakers, being able to distinguish the differences of what AI tech actually does and is, is crucial. Not all AI technologies are the same. I am aware that AI policy discussions today surround inequity, and discrimination—and I agree that AI tech does need to be configured in a way that does not contribute to any harm—but there are also a lot of amazing solutions that society is coming to rely on that wouldn’t be possible without AI. It’s important for policymakers to be careful when thinking about how to handle technologies that are still evolving.
I think an initiative like the National AI Research Resource (NAIRR) is very much needed to help startups and enhance innovation. Machines can only become smarter and make accurate predictions based on quality data. And who has access to quality data is a real challenge in the equitable development of new AI systems. Larger companies have access to this type of data already, but startups don’t have that same advantage. Complex, quality datasets are very expensive, so resources like the NAIRR are critical for startups to grow and innovate. It would definitely be very helpful for startups like us to have policymakers continue to push for the development of these types of publicly available resources.
We should be looking for ways to promote or encourage the use of technology that will enhance the use of data without sacrificing privacy. In many cases, this comes down to cost because right now the cost of obtaining training data is very high. And it's difficult for small startups to get access to quality, private data to build a good algorithm around. I think the NAIRR initiative is going to be a significant step in this space. The industry and policymakers need to ensure that in the process of creating any privacy regulations some AI startups are not unfairly included for the reason that the policymakers don't fully understand the nuances of different technologies. There are real concerns to address here, but I think if people make the effort to work together we can find achievable solutions without sacrificing startup success or compromising data privacy.
You mentioned concerns about making sure that AI systems aren’t excluding or harming people. How are you proactively working to ensure that your AI is not serving any one community better than another?
If you go to our company website, we actually state that our mission is to particularly serve and lift up underprivileged groups. I started to Enployable in 2020 during the pandemic when I saw my friends get laid off and the challenges they faced finding the right job. A huge majority of people who apply for a job never have the chance to have their resumes even looked at—not to mention determine if they are a good fit—because of the way the employers' current screening system is structured.
One quick way we combat this effect is we require employers that use our platform to express why they care about diversity and inclusion. And candidates have space to express what they care about when it comes to diversity and inclusion in the workplace. We put those factors before the resumes and other traditional screening factors in our systems. Additionally, we do not include any kind of traditional demographic or identifying factors that could potentially disadvantage certain groups of people. Finally, we present all candidates to the employers. After that, employers can go look at the candidate’s work experience—those traditional factors people look at—and layer that on top of the information we provide to find the people that are truly a match for the role.
It's sort of a two-prong approach to combat any unfair automated biases. I believe that our platform allows candidates a better way to express themselves, rather than just submitting a resume and being screened out because of certain keywords or other automated searches. It's much more efficient on both sides to be able to find each other if we focus on deeper factors that matter.
What are your goals for Enployable moving forward?
Our ultimate goal is to help people have fulfilling careers where they can continue to grow and be happy. And that's what we prioritize in our work at Enployable. Our hope is also that AI will ultimately help us understand other humans better for the well-being of our future job markets and employment systems. That's the work we do.
All of the information in this profile was accurate at the date and time of publication.
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