Emrick Donadei, a 32-year-old software engineer at Google based in New York, successfully transitioned from a traditional software engineering role to AI safety by leveraging internal company hackathons. When ChatGPT launched, Google intensified its focus on large language models (LLMs), creating new opportunities for employees to pivot into AI roles.
Initially, Donadei felt unqualified for AI positions, lacking what he perceived as the right credentials and hands-on experience with AI products. However, his participation in Google’s annual employee-only hackathon in 2024—a seven-day intensive competition—became the catalyst for his career transformation. During the event, he built a prototype that helped him understand AI fundamentals, including LLM infrastructure development, algorithm creation for agentic workflows, and model fine-tuning.
While Donadei admits his initial prototype “wasn’t super useful,” the hackathon provided crucial hands-on experience and demonstrated his capability to work with unfamiliar technologies. He participated in a second hackathon in 2025, which led to publishing a public technical disclosure with Google.
Critically, Donadei emphasizes that hackathon participation alone isn’t sufficient for career transition. He and his team engaged in extensive self-promotion, presenting their prototype to numerous teams and conducting one-on-one meetings with group technical leads. This proactive networking approach created connections and opportunities that ultimately facilitated his role change.
To accelerate his learning, Donadei leveraged multiple AI tools including Claude Code for reading code and documentation, Gemini and ChatGPT Deep Research for case studies, and NotebookLM for information consumption. He also studied Andrej Karpathy’s YouTube videos and co-hosts a podcast for software engineers and AI enthusiasts.
Donadei’s story illustrates how internal mobility programs and hackathons can democratize access to AI careers within tech companies, particularly during periods of high demand. His transition from traditional software engineering to AI safety engineering demonstrates that formal AI credentials aren’t always prerequisites when combined with initiative, hands-on learning, and strategic networking. The hackathon provided him with “unlimited access to frontier technologies and a direct line to key decision-makers,” proving he wasn’t “late to the AI revolution.”
Key Quotes
I think hackathons are the best way for everybody to get into AI. I’m completely switching my career and that’s all because the hackathon forced me to get my foot in the door.
Emrick Donadei, a Google software engineer, explains how participating in internal hackathons became the pivotal factor in his career transition from traditional software engineering to AI safety, demonstrating the practical value of hands-on learning experiences over formal credentials.
You can’t just do a hackathon and stop there. You have to actually leverage that experience.
Donadei emphasizes that hackathon participation alone is insufficient for career transition. He stresses the importance of follow-through, including self-promotion, networking with technical leads, and presenting prototypes to multiple teams to create opportunities.
Initially, I didn’t think I could get a role working in AI. I didn’t have the right credentials and when I spoke with other teams, I felt a disconnect because I hadn’t even touched the products, let alone experimented with creating them.
Donadei describes the initial imposter syndrome and credential barriers many engineers face when considering AI roles, highlighting a common challenge in the industry that hackathons can help overcome through practical experience.
By granting me unlimited access to frontier technologies and a direct line to key decision-makers, the hackathon proved that I wasn’t late to the AI revolution and gave me the technical confidence to bridge the gap from traditional engineering to LLMs.
Donadei summarizes how Google’s internal hackathon provided both technical resources and networking opportunities that were instrumental in his successful career pivot, demonstrating the multifaceted value of such programs beyond just skill development.
Our Take
Donadei’s journey reveals an often-overlooked reality in the AI talent shortage: companies may already employ engineers capable of transitioning to AI roles with the right support structures. His success challenges the narrative that AI careers require PhDs or specialized degrees, instead highlighting practical experience, initiative, and networking as viable alternatives.
Particularly noteworthy is his transition specifically to AI safety—a field desperately needing diverse talent as AI systems become more powerful. His background in traditional software engineering may actually provide valuable perspectives on building robust, reliable systems.
The strategic use of AI tools to accelerate his own learning creates an interesting meta-narrative: using AI to learn AI. This approach may become standard for future career transitions. However, his emphasis on persistent self-promotion and networking reminds us that technical skills alone remain insufficient—career advancement still requires visibility and relationship-building, even in meritocratic tech environments.
Why This Matters
This story highlights a critical pathway for traditional software engineers seeking to transition into the rapidly expanding AI sector without formal AI credentials or advanced degrees. As companies like Google aggressively expand their AI capabilities following ChatGPT’s disruption, internal talent mobility becomes increasingly important for meeting demand.
Donadei’s experience demonstrates how hackathons serve as practical bridges between traditional engineering and AI roles, offering hands-on experience that formal education often cannot provide. This matters for the broader tech workforce, as millions of engineers may feel pressure to upskill or risk obsolescence as AI transforms software development.
The emphasis on AI safety is particularly significant, as this emerging field requires diverse perspectives and talent to address critical challenges around responsible AI deployment. His success story also reveals how self-promotion and networking remain essential career skills, even within large organizations with internal mobility programs.
For businesses, this illustrates the value of internal hackathons as talent development and retention tools during technological transitions, potentially reducing expensive external hiring while building institutional AI knowledge.
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