For more than a decade, Rebecca Kimble worked as an emergency medicine physician after graduating from the Albert Einstein College of Medicine in 2009. She treated patients and saved lives in emergency rooms across the United States, from Indigenous reservations in South Dakota to a small town in rural Maine, earning between $300,000 and $500,000 a year.

"I enjoyed working where other people didn't like to work," Kimble told the Guardian. "I wasn't afraid to go to the middle of nowhere and work with people who didn't have much."

In February 2022, her career in emergency medicine began unraveling. First, Kimble stepped away from clinical work for a few months after a DUI placed her on administrative leave. When she was legally cleared to work again later that year, she was diagnosed with breast cancer. The surgery and radiation treatment kept her away from clinical work for more than two years.

When she was ready to return, Kimble expected her experience to carry weight. Instead, she kept getting rejected when she applied to roles. Extended gaps in practice can make it difficult for physicians to re-enter the workforce, as hospitals often prioritize candidates who are actively practicing.

The realization that she might not return to the emergency room was devastating.

"That was absolutely horrid," Kimble said. "Each time you try and it doesn't work, you go through that process of fighting depression and anxiety. I thought: 'Oh my God, this is it. This part of me died, and I wasn't ready for it to die.'"

Pursuing a master's degree in public health at Brown University and establishing her own consultancy did little to improve her job prospects. Then her mentor suggested AI data training.

By January, Kimble was juggling AI training assignments on several platforms, evaluating how models responded to medical questions. She described the shift as a "phenomenal transition" that allowed her to combine medical expertise with analytical work. Even so, she would return to emergency medicine if given the chance.

"If you give me a job tomorrow that I think I can do, I'll go do it," she said. "I miss the ER."

Rather than fixed shifts, she operates within a gig-style marketplace where assignments appear without warning and are claimed by whichever workers log in first. When she first started, she sometimes woke up at 4am to check for new work.

"This is not a job," she said. "This is a gig."

Typically, Kimble logs six to nine hours a week doing AI data training. She earns between $500 and $1,000 a month on assignments, with hourly pay ranging from $30 to $140. Sometimes her AI workload can jump to 25 to 30 hours. Other weeks, the work disappears entirely.

To supplement her gig work, she started working part-time as a veterinary technician at the pet clinic where she takes her dogs. Kimble says she is fortunate that her husband covers most of their household bills and she has savings.

AI training "is a temporary fix", she said. "But it's not something that I could count on."

As AI gets better at medical guidance, she worries hospitals could use it to cut the number of doctors on staff. At the same time, she believes she has some agency in shaping the technology. Rather than resisting AI, she argues that doctors should engage with it. By helping train these models, physicians can steer them toward more accurate and responsible medical responses.

"I don't think it is the enemy," she said. "I think it's inevitable."