People often ask why I left a burgeoning career in law to work for Mercor. After all, I liked practice (which is, unfortunately, a rather rare sentiment). So why leave a stable career path that I’d spent half a decade building and move myself and my husband across the country to become a Strategic Projects Lead? The truth is that I became impatient: impatient for impact, impatient for growth, and impatient to be at the frontlines of a technology that was already changing the world, with far more to come.

Over the last few years, I saw intermittent glimpses of what AI could mean for humanity. It is a fundamental fact that most professions critical to the public good—healthcare, legal defense, teaching—are woefully understaffed. And, for the first time, it seemed like we were on the verge of a technological wave that could truly (and quickly) help fill those gaps. As a Second Circuit law clerk, I would sometimes compare pro se legal briefs (those written by individuals without an attorney, almost always because they cannot afford one) to the arguments AI models made if I fed them the (PII-scrubbed) relevant legal questions. While the models would occasionally hallucinate, their legal arguments were almost always better than those in the pro se briefs. They were, however, still far below the quality of legal analysis that the $1k+-an-hour lawyers at white shoe firms could produce. I got into the human data space—where people create the data that trains AI—because I wanted to help models deliver top-notch legal analysis, finally giving the Davids of the world the sling they need to take on the Goliaths.

And I joined Mercor specifically because of its incredible position leading a key transformation within that space: a shift from simple data labeling (e.g., deciding whether an image is of a stop sign or a child) to semantic intelligence work (where experts generate complex tasks, which can take 20+ hours to complete, based on their domain-specific knowledge). The company’s core thesis was that, to create next level AI models, you need great data and, to create great data, you need top human talent. Thus, Mercor’s founders focused on (1) building products that could effectively and efficiently screen for true domain experts (products which have use cases that go far beyond the human data space) and (2) creating a deep bench of those experts across a broad range of domains.

The company’s success in this new era of semantic intelligence work has made my job as a Strategic Projects Lead incredibly exciting for three reasons: (1) we work for the best clients; (2) we engage the best experts; and (3) we become the best operators. Mercor now works with the top five AI labs and six of the Magnificent Seven; many of the projects we run for these companies involve the world’s best and brightest, including Nobel Prize recipients, Emmy winners, Marshall and Rhodes Scholars, FAANG software engineers, and IMO medalists. To make these projects successful, Mercor also invests in world-class internal talent. My colleagues are among the sharpest and most committed people I’ve had the privilege of working with. And we’ve all grown into even stronger operators (and engineers) by taking on far more challenging and meaningful problems at Mercor than we could find elsewhere—backed by exceptional mentorship from leaders like the Mercor founders, who built a company that has engaged 10,000+ experts from 45+ countries in under two years, and Sundeep Jain, Mercor’s President and former Chief Product Officer at Uber.

To put things in perspective: in my second week at Mercor, I was tasked with staffing and running a project of 200+ software developers for one of the world’s top AI companies. This project not only created thousands of high quality data samples that will move the needle on AI progress—it also gave hundreds of people flexible, high-paid work for nearly five months, with a total of over $4,000,000 DPT (Dollars Paid to Talent). In my second quarter at Mercor, I interfaced almost daily with one of the top researchers at the same frontier lab to manage what the lab has referred to as one of the most ambitious human data projects it’s ever undertaken (notably led by an all female team, on both the lab’s side and Mercor’s). In sum, I can’t think of a better place to both learn about the human data space, with a bird’s eye view of how the best AI companies utilize human data to improve their models, and to challenge myself as an operator, responsible for supporting hundreds of experts and executing projects worth millions of dollars.

Looking back, the decision to leave law wasn’t about walking away from anything: it was about running toward something urgent and somewhere that I could flourish. I wanted to be in a place where innovation was the norm, where I could take full ownership from Day One, and where the work could scale beyond a single courtroom or client to help shape systems at the global level. Mercor has been that place.

For anyone standing at a similar crossroads who is curious about how they can contribute to something bigger, faster, and more impactful, I hope my story offers a glimpse of what’s possible. If you’re drawn to the same things that I am—a desire to have global impact; to work on the cutting edge of developments in AI; and to push the boundaries of your capabilities—I’d encourage you to check out our open internal roles.