As artificial intelligence continues to dominate the tech industry, professionals are sharing their unique pathways into this competitive field. Three AI professionals recently revealed how strategic use of higher education helped them successfully transition into lucrative AI careers at major companies and startups.
Varun Goyal, a 25-year-old AI startup engineer in California, made the bold decision to leave quantitative trading and return to school for a master’s degree in computer science. After working as a quantitative strategist in India, Goyal felt compelled to explore the burgeoning AI boom more deeply. His graduate program provided crucial opportunities to conduct research and engage with senior industry professionals, helping him decide between quant trading and AI. Despite accepting a lower base salary than he would have earned in quantitative finance, Goyal joined an AI startup in 2024, believing AI offered more long-term career options. He credits his master’s program with making this career pivot possible.
Deep Shah, a 30-year-old software engineer at Google in Mountain View, emphasized the critical role of mentorship and networking throughout his educational journey. Initially interested in developing computer games, Shah discovered machine learning through professors who exposed him to AI problems during his bachelor’s degree. These mentorship relationships taught him skills rarely learned through coursework alone and significantly enhanced his résumé. After joining Google Bangalore in 2018 through a friend’s referral, Shah leveraged his network again in 2021 to relocate to Mountain View and join the team improving Google search user experience. His story demonstrates how relationships formed during college continue shaping careers long after graduation.
Kriti Goyal, a 28-year-old AI machine learning engineer at a Big Tech company in Seattle, used her master’s degree as a strategic pathway to both advance her career and relocate from India to the United States. Now part of the Foundation Model main framework team, Goyal has held four different roles over five years with her company. She chose the master’s route over internal transfer because of the specialized knowledge and networking opportunities it provided. By reaching out directly to contacts from her previous internship in India rather than applying through job boards, she secured her machine learning engineering internship easily. While acknowledging that networking can happen outside universities, especially in tech hubs like San Francisco, Goyal notes that hiring bias still exists for specific teams, and her master’s degree helped navigate both the immigration system and cultural transition.
Key Quotes
Returning to school gave me the opportunity to pursue research and engage with senior industry professionals in both fields. This was the biggest benefit for me when I was deciding what I wanted my daily life and career to look like in 10 years.
Varun Goyal, AI startup engineer, explaining how his master’s program helped him evaluate career options between quantitative trading and AI. This highlights how graduate education provides exploration opportunities before committing to a specific career path in the competitive AI field.
Working with a mentor is also a valuable addition to your résumé, demonstrating that you already possess the skills and experience necessary to succeed in a professional environment.
Deep Shah, Google software engineer, emphasizing the practical value of mentorship during college. This quote illustrates how mentorship relationships provide both tangible résumé benefits and intangible skill development that traditional coursework cannot replicate.
I had two ways to go about moving to the US. One was to try to move within my company or pursue a master’s degree. Two reasons I chose the master’s path are the knowledge and extra specialty you can develop through projects, as well as the connections you make.
Kriti Goyal, AI machine learning engineer at Big Tech, explaining her strategic decision to pursue a master’s degree for career advancement and international relocation. This demonstrates how graduate programs serve multiple purposes beyond education, including immigration pathways and networking opportunities.
Learning and networking can be done in many places; it doesn’t have to be university. In a city like San Francisco or New York, you could hustle and get the networking benefits of a university and a structured system. I think it’s now possible to skip that education stage. But I have seen a bias in hiring for specific teams, and it’s not unbreakable yet.
Kriti Goyal acknowledging alternative pathways into AI while recognizing persistent hiring biases. This nuanced perspective reveals that while AI education is becoming more accessible, traditional credentials still provide advantages in certain contexts, particularly for international professionals navigating immigration systems.
Our Take
These career trajectories reveal a fascinating tension in the AI industry between meritocracy and credentialism. While AI has been celebrated as a field where self-taught practitioners can thrive, these stories suggest that strategic use of formal education still provides significant advantages, particularly for international talent and those making major career pivots.
What’s particularly noteworthy is the emphasis on networking over technical skills alone. All three professionals credit relationships—with mentors, peers, and former colleagues—as pivotal to their success. This suggests that as AI becomes more democratized through online courses and open-source tools, the differentiator increasingly lies in access to professional networks and insider knowledge about opportunities.
The willingness to accept lower compensation for better long-term positioning in AI also signals strong confidence in the field’s future growth. As AI continues reshaping every industry, these early-career investments in specialized education and strategic positioning may yield substantial returns.
Why This Matters
This article provides crucial insights into the competitive landscape of AI career development at a time when artificial intelligence roles are among the most sought-after positions in tech. The stories highlight three distinct but equally valid pathways into AI: career pivoting through advanced education, leveraging mentorship networks, and using graduate programs for international mobility.
The experiences shared reveal important trends about the AI job market. First, professionals are willing to accept lower initial salaries in AI startups versus established fields like quantitative trading, betting on long-term career flexibility and growth potential. Second, networking and mentorship remain critical differentiators in landing competitive AI roles at companies like Google, often more valuable than traditional job applications. Third, despite the democratization of AI education through online resources, formal degrees still provide advantages in hiring, immigration, and accessing core decision-making roles at major tech companies.
For aspiring AI professionals, this article underscores that while multiple pathways exist, strategic use of education combined with intentional relationship-building significantly accelerates career entry and advancement in this rapidly evolving field.
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Source: https://www.businessinsider.com/how-three-people-used-college-to-break-into-ai-2025-12