Meet a Text IQer: Senior R&D Engineer Daven Corbett

Meet a Text IQer: Senior R&D Engineer Daven Corbett

We sat down with Daven – who studied Computer Science at Stony Brook University and comes to us via enterprise software companies like Salesforce – to discuss high stakes, custom query languages, and cool co-workers.

How did you end up at Text IQ?

I only started last Summer, but in Text IQ time that’s actually quite a while. The team around me has quadrupled since I joined, and we’re getting ready to quadruple again. In my last role, I was working with Salesforce, when one of the engineers at Text IQ reached out to me. The reason I reached back out was because I wanted to find a company that would give me a more hands-on role working with AI. The guy who reached out told me Text IQ was busy trying to build something that doesn’t exist yet: AI that can understand sensitive business data at scale. And so the conversation started.

What was the interview process like?

Early in the interview process I found myself talking to the CEO. I loved that. I actually believe that the interview process tells you a lot about the company. Here I was with a solid line to Apoorv. Our first conversations were about vision and goals.These things haven’t changed. He works closely with the Engineering team, and his working style is to direct the vision and invite us to contribute.

What is it like to work so closely with the founding team?

I’ve noticed a pretty interesting pattern. First we receive a vision that seems difficult to achieve. Then as time goes on, Apoorv will lay out clear and fundamental steps for how to accomplish it. And then we invariably do, and at the end of this process, I think: “I can’t believe you were able to see this at the very beginning.”

A lot of our team’s confidence simply comes from having so many experts in mathematics and software development, along with all these PhDs in machine learning. This is critical: our customers are choosing us because the complexity of their problems can only be solved with the most sophisticated technology.

What are the big problems you’re solving?

The last challenging problem I worked on was building a GDPR solution for one of the world’s most famous companies. We knocked that out of the park, so now we’re building it into a complete enterprise solution.

Now our focus is on The AI Brain. That came out of general conversations that Ethan, our Director of Research, was having with our customers. There was a vision of how valuable it would be to give our customers an ongoing dashboard of their risk data. “Sensitive information management.” We are already seeing The AI Brain change how businesses work. What’s so magical is that the value is so clear and present, but also so hard to describe. I think that’s a good thing. It means we’re pulling a sword out of the stone.

What’s the coolest technique you’re working with here?

Lately I have been working with our custom query language. We wanted to give users a way to gain insight into the massive dataset we work with, so we created a domain-specific query language for non-programmers, which compiled to an internal representation that finds documents of interest. This is instrumental in allowing our users to better navigate their data.

But it’s the ability to read natural text, taking into account the changing roles and relationships of the people behind this text, then understanding what’s going on, that directly solves for the huge privacy problems that enterprises are facing. It gets me excited to come to work. Every day brings a new technical challenge. How do you identify an entity inside short text, or a never-before-seen type of text, or in even outside of language and words? We joke that we might have to rename the company soon as we continue to expand beyond text, and look at more and more kinds of data.

What’s it like to work with our customers?

I’m passionate about enterprise and B2B. Our customers have interesting problems that demand enterprise solutions. So much collective effort is spent solving problems for consumers. But the minute I got into saas I was struck by how enterprises have such a different set of problems.

I feel that our technology is bringing a new approach to solving business problems. It’s not just our approach, it’s the problems themselves that are new. The things we’re solving for aren’t even in the lexicon yet, like “enterprise disasters.” Being this far forward on the edge is exciting.

We need to provide a solution that’s utterly trustworthy. There will be real-world consequences if it’s not. I’m humbled by the trust our customers place on us. These are companies in the news, so our team and our technology are having an enormous impact.

How is Text IQ different from your last jobs?

In my previous roles, the stakes were that maybe we received an angry email, or a customer got overcharged. Here the stakes are much, much higher. My colleague Weichen said that he loves the scale of Text IQ, and described scale as a feature. Well, I think that high stakes are a feature.

Typically, an engineers will deploy a project at night and deal with errors the next morning. I think there are barriers between them and the customer, and the barrier is connected to low stakes.

This job is different. Because the stakes are high, we’re literally in constant sync with the customer. We’re literally with them every step of the way. This requires more communication and it gives us a window into their deepest concerns. Since we’re identifying and managing sensitive information, customer success here takes on a new meaning: it literally means the success of the customers’ brands.

What’s your gut impression of Text IQ’s technology?

Every time that we’re given a Proof of Concept and it turns out that we were able to find additional sensitive information that the original human teams missed, I’m amazed. It’s a kind of marvel. Our platform allows us to accomplish so much more with so much less. Our results are usually more accurate and less time-consuming than the manual workflow. I’m excited to be part of the team that’s building it out.

How would you describe your co-workers?

We’re basically a team of researchers who have stumbled into an enormous market opportunity. We have huge goals and a small team. And I love everyone here. There are so many PhDs, people with deep saas experience, and people with Masters degrees in NLP and ML. As I say this just now, I’m starting to realize how our CEO expects to accomplish these massive goals. He hires great people.