Staffing Built for the AI Age, Wael Sabra

What we talked about.

Sunny Ray sits down with Wael Sabra, co-founder of Sierra, a staffing platform built for the AI age. Sabra explains how Sierra standardizes the fragmented process of finding, hiring, paying, and managing talent through a single platform called Sierra 1, cutting hiring timelines from months down to 48 hours. Drawing on his background running technology and product at A+E Networks during the rebuild of television advertising, Sabra shares how he learned that technology leadership is really a people job, and how data taught him to spot the early signals of a good hire. He describes the moment a CFO nearly stripped him of open roles because location restrictions made talent impossible to find, and argues that opportunity, not talent, is the real scarcity. The conversation covers Sierra's matching algorithm, which borrows principles from dating apps and integrates with systems like Jira, plus a skeptical take on AI interview agents layered onto broken hiring processes. Sabra closes by urging companies to treat candidates the way they treat customers.

Sierra co-founder Wael Sabra on fixing broken hiring with data, matching algorithms, and human-centered AI.

The questions, and the answers.

What is Sierra and what problem are you solving?

Staffing is built backwards, done differently by different people at every company. We standardized the entire process of finding, hiring, tracking, paying, and managing talent into one platform called Sierra 1. It is the golden age for technology but the stone age for hiring, and we are changing that. A hiring transaction that used to take months now takes 48 hours.

You ran technology and product at A+E while television advertising was being rebuilt. What did that period teach you about teams?

I did not grow up in staffing, but as a technology executive I estimated 60 to 70 percent of my time went into talent activities, finding the right partner, recruiting, or convincing someone not to leave. The higher I moved up, the less involvement I had with technology itself and the more it was about growing and retaining the team. That was my light bulb moment.

You went from analytics manager to VP to SVP of product in five years. What changed in how you saw the work?

Starting on the data side taught me that exploring a company's data reveals a lot about its business. I started applying that lens to hiring, looking at people through data while still appreciating the human touch. I noticed people repeated the same hiring mistakes multiple times, and I wanted to catch those early signals before they became costly.

What was the moment that convinced you the way companies hire was holding them back?

A CFO told me I had three open roles for three months and they were taking those roles away because it looked like I did not need them. In reality the company required hiring in a specific office location, which made it impossible to fill the role fast. I could have hired that person halfway around the world in 24 hours.

Talent is everywhere but opportunity isn't. Where did you see that gap most sharply?

I saw a company try to build a call center, hands on keyboard, data entry type operation in New York instead of a city built for that kind of talent and space. It reached maybe 30 to 40 percent of the success it could have had elsewhere. It was a glaring example, but this happens in less obvious ways constantly.

Your platform lists a quarter million professionals and 12,000 prevetted. Which number predicts a good hire?

What predicts a good hire is connecting our platform to outcome systems like Jira or Salesforce, lining up hours and cost against actual results. That lets us find a lookalike profile of your most productive person. We also weigh company culture, industry, and values, using matching principles similar to what dating apps run at scale every day.

You sell AI enablement alongside the people. Are you selling the labor and the thing that replaces it?

Yes. We are not an AI lab, but we apply AI from a human angle since we understand both technology and staffing. Instead of five developers, a client might get an agent built by our team plus one developer to maintain it. So we sell both the labor and the technology that augments it, not replaces it.

Where do AI agents actually change staffing over the next two years, and who loses first?

Only about 3 percent of applicants are truly interested in a job through to the end, yet companies use AI to interview everyone faster on a broken process. We have seen candidates get the same rejection whether or not an AI interview happened. The fix is treating candidates like users, not running a broken funnel faster with agents on top.