Despite the changing landscape of tech education, both traditional higher education and strategic career moves remain vital for securing a role in AI engineering, according to Kriti Goyal, a successful machine learning engineer based in the United States.
Kriti Goyal’s journey into the realm of artificial intelligence and machine learning began in the unlikely setting of Bikaner, a small town in Rajasthan, India. Initially inclined towards medicine, her trajectory changed after watching a pivotal video presented by tech leaders like Mark Zuckerberg and Bill Gates. This video illuminated the power of coding as a tool to transform ideas into tangible products, setting Goyal on a path that would lead her to a prominent role in a major U.S. tech firm.
Currently a member of the Foundation Model framework team, Goyal plays a critical role in constructing the foundations of machine learning models. Her work involves developing code that enables software to identify and generate patterns from unrecognized data, a task integral to the advancement of machine learning applications.
Goyal’s professional journey began with an internship in India at the same company where she is now employed in the U.S. Although she enjoyed her time working in India, she realized that core strategic decisions were predominantly made at the company headquarters in the United States. This realization fueled her decision to move to the U.S. in pursuit of professional growth.
The pivotal decision to pursue a master’s degree was instrumental in facilitating her transition to the United States. Goyal valued the advanced knowledge acquired through her master’s program at the University of Wisconsin-Madison, but she also emphasized the importance of networking. The connections made during her studies proved advantageous when she reached out to former colleagues and managers, ultimately easing her path to securing a machine learning internship.
Her proactive approach during her internship included pitching internal projects to various teams, a strategy that played a significant role in her securing a full-time role in AI engineering. Her current role as a machine learning engineer involves a daily routine of research, team collaborations, and coding—a balance she finds rewarding.
Goyal acknowledges the evolving nature of tech education, noting that while higher education remains beneficial, it’s not the only pathway to success. She highlights a noticeable bias in hiring practices that favor candidates with advanced degrees, but also recognizes the potential to bypass traditional pathways through networking and proving one’s skills. Goyal suggests that environments like San Francisco and New York offer opportunities to replicate the networking and structured systems traditionally provided by universities.
This multifaceted approach reflects Goyal’s perspective that while academia can offer advantages, particularly in teaching, tech professionals can also succeed by demonstrating their abilities and adapting to the dynamic demands of the industry.
Source: Original article