As artificial intelligence continues to reshape the tech industry, Google employees are increasingly pivoting to AI-related roles, with many spending a year or more building the necessary skills for the transition. Business Insider interviewed four Google workers who successfully made the shift to AI teams, revealing the dedication and strategic approaches required to break into this competitive field.
Emrick Donadei, a 32-year-old software engineer now working on AI and machine learning safety, credits Google’s seven-day employee hackathon in 2024 as his breakthrough moment. Though he didn’t create a revolutionary product, the hands-on experience provided tangible proof of his capabilities. Over 10 months, Donadei supplemented his hackathon work by creating an AI podcast and studying Andrej Karpathy’s YouTube videos to master machine learning and LLM concepts. His persistence paid off with opportunities in AI research and open-source committees.
Maitri Mangal, 27, took a different approach by dedicating two hours daily to upskilling during her year-long transition to Google’s Workspace AI team. She leveraged social media content creation as a learning reinforcement tool, finding that teaching others through videos motivated her continued education. Even after landing her new role, Mangal maintains an hour of daily learning through internal trainings and YouTube courses.
Rahul Kasanagottu, 32, invested the longest timeline—two and a half years—to become a customer engineer specializing in AI and machine learning. He read 11 books on AI, completed Andrew Ng’s Deep Learning Specialization course, and watched 3Blue1Brown videos. Kasanagottu emphasized that solo projects and demos were crucial for convincing hiring managers of his capabilities.
Milica Cvetkovic followed a more academic path, completing a Master’s in statistics and conducting machine learning research before joining Google’s AI consulting team three years ago. Her unique value proposition was her ability to communicate technical concepts in non-technical language, developed through teaching machine-learning boot camps and college courses. Unlike the others, her transition was less deliberate and more about finding the right opportunity that aligned with her background and desire to move away from coding.
Key Quotes
That really, for me, changed everything
Maitri Mangal said this about content creation as a learning tool. Her approach of creating social media videos to teach AI concepts not only reinforced her own learning but also motivated her to continue studying, demonstrating how teaching others can accelerate personal skill development in technical fields.
Having a skill to talk in a nontechnical way is probably the most valuable skill that I bring
Milica Cvetkovic emphasized this unique capability that distinguished her in AI consulting. This quote highlights an often-overlooked aspect of AI roles—the ability to bridge technical and business audiences—which can be as valuable as coding skills in certain positions.
It was difficult to convince hiring managers he could do the job without having demos and hands-on projects to show
Rahul Kasanagottu explained why solo projects were essential to his transition. This underscores that theoretical knowledge from books and courses alone isn’t sufficient; hiring managers need tangible evidence of practical AI skills through working demonstrations and real-world applications.
That’s literally what my application was. It was just very good fit
Milica Cvetkovic compared her career journey to training for a marathon, where the job application represented the culmination of years of preparation. This perspective reframes career transitions as long-term investments rather than quick pivots, emphasizing patience and consistent effort.
Our Take
These Google employees’ experiences reveal a sobering reality about AI career transitions: there are no shortcuts. The one-to-three-year timelines contradict the hype suggesting workers can quickly pivot into AI roles through brief courses or certifications. What’s particularly noteworthy is the diversity of successful approaches—from hackathons to content creation to academic research—suggesting multiple viable pathways exist.
The emphasis on continuous learning even after landing AI roles indicates this field’s rapid evolution requires perpetual upskilling. Mangal’s continued daily learning and Donadei’s ongoing hackathon participation demonstrate that AI professionals must commit to lifelong education.
Most striking is Cvetkovic’s insight about non-technical communication skills. As AI becomes more embedded in business operations, professionals who can translate between technical teams and business stakeholders may become increasingly valuable. This suggests that the AI job market isn’t exclusively for hardcore engineers—there’s room for diverse skill sets that complement technical expertise.
Why This Matters
This story illuminates the practical realities of entering the AI job market at a time when companies are investing heavily in artificial intelligence capabilities. The experiences of these Google employees reveal that AI career transitions require substantial time investment—typically one to three years—challenging the notion that workers can quickly pivot into these high-demand roles.
The article highlights critical skills and strategies for aspiring AI professionals: hands-on projects through hackathons, continuous learning through online courses and content creation, and the ability to communicate technical concepts effectively. As AI continues to transform industries, understanding these pathways becomes essential for workers seeking to remain competitive.
The emphasis on self-directed learning and practical demonstrations suggests that traditional credentials alone may not suffice in the AI field. This has broader implications for workforce development and corporate training programs, indicating that companies need to provide structured opportunities like hackathons while employees must take personal responsibility for upskilling. For the tech industry overall, these stories underscore the talent shortage in AI and the lengths both workers and companies must go to fill critical roles.
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Source: https://www.businessinsider.com/googlers-share-transition-to-ai-roles-2026-1