After nearly two decades in biotech, Ruby Cheung didn’t expect artificial intelligence to be part of what came next.
For most of her career, she worked inside large, complex scientific organizations, helping move pharmaceuticals from early research through testing and, eventually, into the market. The work demanded precision and patience. Over time, she found herself drawn less to the bench and more to the connective tissue around it—the planning, coordination, and judgment required to keep long, multi-year efforts moving.
“I was able to see pharmaceuticals going from basic research, through testing, all the way to market,” she says. “Knowing I was part of that pipeline was very satisfying.”
Then the industry slowed. Layoffs rippled through biotech, and Ruby was laid off for the first time in her career.
She took it as opportunity to pause. She traveled. She spent time outside. She let herself think about what kind of work she wanted to do next—and what she didn’t.
That reflection eventually led her somewhere unexpected.
A Career Built on Judgment and Systems
Ruby’s career has never followed a single, straight line. She double-majored in science and music, studying classical piano while preparing for a future in biotech. During school, she taught piano on weekends to cover rent and books. The balance—discipline paired with creativity, endurance paired with structure—became a pattern she carried with her.
In biotech, that meant moving from academic research into industry roles where she could see how decisions upstream affected outcomes years later. She gravitated toward operations and project management, translating complex scientific work into systems that could actually function across teams.
She learned about Mercor through a friend and assumed, at first, that it wouldn’t be relevant. Her background was biotech. The work was AI.
But the application process felt familiar.
“All I had to do was write about tasks I’d actually done in my career,” she says. “That part was very natural.”
Working Behind the Scenes of AI Systems
Through Mercor, Ruby began contributing as a senior domain expert on project-management-focused AI initiatives. The work centers on real professional workflows: how projects are scoped, how constraints are handled, where tradeoffs show up, and how decisions get made when information is incomplete.
“Now that I’m behind the scenes,” she says, “I understand how much human judgment goes into how these systems reason and respond.”
In biotech, progress is incremental. Systems are tested, refined, and improved over time. Oversight matters. Context matters. Nothing meaningful happens without people paying close attention.
“I like seeing something start at the beginning and move forward,” Ruby says. “I want to know my work isn’t just busywork.”
That sense of continuity is important to her. So is closure.
“Every time I submit something, it’s done,” she explains. “I like finishing things.”
Why Human Expertise Still Matters
For Ruby, that means applying the judgment she’s built over decades—grounded in real systems, real constraints, and real outcomes—to help shape how AI systems behave in the world.
Her story reflects a simple truth about how AI actually improves: it doesn’t happen automatically. It depends on people who understand how work really gets done, where judgment matters, and what complexity looks like in practice.
That’s the kind of expertise Ruby brings and why it still matters.