Why I Joined Mercor

Peter Zhang, Mercor Engineering Lead, shares why he transitioned from a career in quantitative finance to be at the forefront of AI.

Why do we pay quantitative traders $300/hr?

We pay them to resolve challenging ambiguity—questions about the world that are difficult to scope and harder still to answer. Markets cannot afford to be wrong about the future of financial capital. Trillions of dollars swing on how the world will look in one, five, and twenty-five years, making markets early bells for world-altering shifts.

The bells have been ringing. AI is the question mark of the decade. Markets are scrambling to assess the scale of the buildout, the potential productivity gains, the ultimate winners. Machine intelligence is rippling through industry and everyday human life, raising new questions about our future. Last year, from the trading floor of Jane Street, I felt the gravity of these questions and frustration at the limits of a trading terminal. I wanted to get closer to the ambiguity.

I wanted to study what AI would mean for human capital. Quantitative finance understands talent better than any industry. Firms live and die by identifying, measuring, and utilizing talent. Well before Jane Street, I wondered whether we could encode our heuristics about how to do work into rules and models. These questions have always been considered too complex, too human, to systematize—so we've built entire fields of organizational psychology and management theory on intuition rather than data, surrendering to subjective judgment what should be measured and understood.

In early 2025, I heard from a former colleague about a startup called Mercor. They were taking the best experts from around the world and pushing the frontier of model capabilities. They were hiring thousands of experts every month across hundreds of industries and verticals. They were operating at a scale where they couldn’t afford to be wrong about human capital. It was the perfect frontier.

Mercor moves fast. I left New York in April. By May, I was describing my work to a friend. "How can you agree to work on something that might even not be possible?" he asked. "There’s no roadmap!" I was working on assessments: how to move past resumes and directly measure skill. These problems felt more like research papers than product specs: What makes an interview “good?” How can AI run and score interviews at scale? How do you quantify performance when you're hiring across industries as different as software engineering and law? "The ambiguity," I told him, "is exactly why I’m here."

Since then, Mercor has run over a million AI interviews and placed thousands of people into roles they wouldn't have found otherwise. Traditional screening would have filtered out many of our top performers—the self-taught engineer in Lagos, the Portuguese lawyer. Our models found them and they're excelling.

These days, I spend my time engineering solutions to new questions. How do we structure teams that multiply rather than average individual brilliance? How do we measure who's truly excelling when simple metrics fail? And how can we automate these processes without losing the nuance that makes them work?

AI is the new center of gravity for the brightest minds. If you’re excited to face challenging ambiguity at the intersection of AI and how we do work, I’d love to work with you.