This essay is based on a conversation with Seattle-based 28-year-old AI machine learning engineer Kriti Goyal and a conversation about his journey into his current role and his daily schedule. Edited for length and clarity.
I grew up mainly in a small town in Bikanah, Rajasthan, India. I always thought I would study medicine until my cousin showed me a video that changed my life.
This is about code.org videos with Mark Zuckerberg, Bill Gates and other Tech Rockstars, which are the quickest way to convert ideas into products. The video was a huge turning point in my life and career.
I am currently part of the Foundation Model Mainframework team of leading major technology companies in the US. I recently completed it for five years with them. During that time I played four different roles.
I moved to the US using my master's degree, furthering my career. However, whether or not a higher degree is necessary is complicated.
Machine learning teams have many roles
Machine Learning Team Ladders have multiple rungs.
Various roles include researchers, engineers seeking machine learning models, and building applications at the top. It also includes core machine learning people developing the actual models themselves. Finally, we will create a Product Center Toolkit that has an infrastructure stack barrier to help machine learning teams.
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I'm working on building the foundations of machine learning models. That is, you build code that recognizes invisible data and trains the software to create patterns.
I started my tech career as an intern in India, but I knew I had to come to the US to move forward.
I originally interned at my current company in India. I enjoyed working in India. The work was great, but understanding the decisions of the core business and strategies for the next project was done here at our headquarters in the US.
I had no intention of moving to the US before. I was very happy in my country. But overall, I have continued to feel like I'm not doing my best in my career as I live far from my core business decisions.
Go further in AI engineering with your internship and master's degree
I had two ways to move to the US. One was to try to move from within my company or get a master's degree. There were two reasons why I chose to become a master. It is knowledge and special specialization that can be developed through projects and connections.
The biggest thing I took away from my master's program at the University of Wisconsin-Madison was definitely the people.
When I arrived in the US, I already knew a few of my previous companies since my time in India, so instead of applying for a job board, I contacted many managers directly. I received a machine learning engineering internship interview very easily as they had known me and my job before.
Solding my product internally helped me to win a full-time AI engineering role.
When I started again as an intern, this time in the US, I did a few things that would help me get into this job. I internally pitched my product to other teams and adopted it. My manager kept telling me that when they were fighting to take me to the office full time, it was the main thing they used.
Now, as an engineer on a machine learning team, I like to divide my day into three parts. It depends on the lifecycle of the project, but usually starts with research. The second part is upstream and downstream check-in with other team members and clients. I talk to people on other teams and say, “Hey, this is what we can do, and is this working for you?”
Everyone's favorite part is third, essentially practical building and coding. Fortunately, most of your time is individual contributors and you can focus on coding.
Higher education in technology remains important, but there are other ways to do it
I think it's possible now to skip that education stage. But I've seen bias towards hiring a particular team, but it's not broken yet.
I was changing countries and cultures, and universities were a great way to get through the immigration system and understand culture. I needed it.
If you want to participate in academia and education, the path to higher education makes sense. But if you want to build something faster, you can learn and network in many places. In cities like San Francisco and New York, you can embrace and get the networking benefits of universities and structured systems.
Basically, you need the ability to prove that you are good at work. It doesn't actually come from the degree. However, I usually find that there are some biases for applicants that are not at a higher degree than the bachelor's degree.
Are there any stories to share about AI or higher education bias in technology? Please contact this reporter, Agnes Applegate aapplegate@insider.com.
