Business & Finance

5 Google employees share how they pivoted to AI roles


Pivoting to an I have a job may be trendy, but that doesn’t mean it’s an easy feat.

As AI-related roles continue to pop up and companies invest heavily in upskilling, more workers are looking to add “AI” to their job titles.

To see how it can be done, Business Insider spoke with five Google employees who transitioned to AI teams. While each followed a different path, many spent a year or so building the necessary skills to land new roles — and for some, the transition took several years.

From participating in employee hackathons to becoming AI content creators, the five Googlers share how they made the shift:

Emrick Donadei


Emrick Donadei looking at phone and laptop

Emrick Donadei is a software engineer working on AI and machine learning safety.

Emrick Donadei



Emrick Donadei said he didn’t feel qualified to pivot to an AI team until he participated in Google’s seven-day employee hackathon in 2024. The 32-year-old engineer said he didn’t create a revolutionary product, but it gave him hands-on experience with tools, and something tangible he could use to start conversations with teams across the company.

Roughly 10 months after his first hackathon, he said he landed his new role.

While the hackathon kick-started his transition, his work didn’t stop there. The Googler continued to experiment with tools outside the hackathon, he said. He also created a podcast about AI developments and watched Andrej Karpathy’s YouTube videos to get up to speed on machine learning concepts and LLMs.

After finding a new role, Donadei said that he participated in another hackathon in 2025, which opened up even more opportunities. He had the opportunity to transition into AI research, began working part-time on open-source committees and with AI research teams, and published a public technical disclosure with Google as a follow-up to his work.

Maitri Mangal


Maitri Mangal standing in front of building

Maitri Mangal switched to the Workspace AI team at Google.

Maitri Mangal



Maitri Mangal, 27, worked as a traditional software engineer before transitioning to an AI team. During the roughly year-long period she took to prepare for the pivot, Mangal dedicated roughly two hours daily toward up-skilling, and she still spends hours learning weekly, she said.

She said that creating social media content was a way for her to reinforce the material that she learned through Google’s internal training and other online courses.

“That really, for me, changed everything,” Mangal said about content creation.

She said seeing that her content helped other people motivated her to continue learning about the technology and making videos. Even though she already changed jobs, she said she still spends about an hour daily learning new information, whether that’s in the form of internal trainings for her job, or watching YouTube courses to prepare for content.

Rahul Kasanagottu


Rahul Kasanagottu smiling headshot

Rahul Kasanagottu now works as a customer engineer at Google, specializing in AI and machine learning.

Rahul Kasanagottu



Rahul Kasanagottu, 32, spent two and a half years transitioning to an AI role at the tech giant. He said his paternity leave gave him a head start on reading about AI.

In addition to reading 11 books on the topic, Kasanagottu also took a Deep Learning Specialization course taught by Andrew Ng, and watched 3Blue1Brown videos on YouTube.

Similar to Donadei, Kasanagottu said solo projects were a key part of his career transition. He said it was difficult to convince hiring managers he could do the job without having demos and hands-on projects to show. While the books he read typically didn’t come with assignments, the courses had a lot of hands-on exercises, Kasanagottu said.

Milica Cvetkovic


Milica Cvetkovic

Milica Svetkovic landed a role in AI consulting at Google after getting her Master’s in statistics.

Milica Cvetkovic



Milica Cvetkovic took a different path than the other Googlers who made internal pivots to AI teams. She landed a role in AI consulting at the tech giant about three years ago, after completing graduate school and conducting research in machine learning.

After she received her Master’s in statistics, she worked as a machine-learning engineer at a Madison-based startup and simultaneously taught machine-learning boot camps and college-level courses.

“Having a skill to talk in a nontechnical way is probably the most valuable skill that I bring,” Cvetkovic said.

Her move to an AI team at Google was less of a deliberate pivot and more the result of the right opportunity aligning with her background and interests. She said that she realized she didn’t want to code anymore, and that’s when she came across a consulting role at Google.

Cvetkovic said she can’t name one single experience that led to her getting the job. Rather, she compared her career journey to training for a marathon. A marathon, she said, is the “celebration of all the work that you’ve done.”

“That’s literally what my application was. It was just very good fit,” Cvetkovic said.

Max Buckley


Max Buckley standing and smiling

Max Buckley completed roughly 40 online open courses.

Max Buckley



Max Buckley, 38, first landed a role at Google in 2013 as a financial analyst. Now, nearly 13 years later, he’s leading an LLM information retrieval applied research team.

He told Business Insider that when he first started at the tech giant, his “North Star” was to become a data scientist. So he began taking online courses to make the pivot.

From 2013 to 2021, he completed roughly 40 online coursesmost through Coursera. He said he didn’t have a particularly structured way of going about it, and mostly did them during evenings and weekends when he had time.

“I don’t think it came at a great cost to me,” Buckley said. “I didn’t feel like I was stressed out or burnt out, or anything from it.”

He also returned to school for multiple degrees, including a postgraduate certificate in statistics, a master’s degree in Business Analytics, a master’s degree in software engineering, and a diploma in Advanced Studies in Data Science.

Buckley’s transition took years, moving from the financial analysis team to business analysis, to trust and safety, and finally to an engineering team in 2016. He then joined several other teams before landing in his current role.

Buckley said he doesn’t regret studying finance because it gave him a business perspective, and he still wound up in computer science. Instead of racing to the finish line, he took courses based on his interests and their relevance to his career at the time. He said his résumé reflects that commitment to continuous learning.

“That’s what I suppose hiring managers or recruiters see in my profile,” Buckley said. “That I’m not someone who just gets complacent.”

Did you transition to an AI role? We want to hear from you. Reach out to the reporter via email at aaltchek@insider.com or through the secure-messaging app Signal at old.19.



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