I'm a 28yearold AI engineer in Big Tech. Here's my advice for others who want to break into this growing field.
- - - I'm a 28-year-old AI engineer in Big Tech. Here's my advice for others who want to break into this growing field.
Agnes ApplegateJuly 18, 2025 at 2:08 AM
Kriti Goyal, 28, used her master's to further her career in Big Tech and move to the USCourtesy of Kriti Goyal -
Kriti Goyal leveraged her master's degree to advance her AI career in the US.
She pitched projects internally during her internship that were adopted to help secure a full-time offer.
Higher education aids in tech careers, but networking and skills can also open doors.
This as-told-to essay is based on a conversation with Kriti Goyal, a 28-year-old AI machine learning engineer based in Seattle, about her journey into her current role and her daily schedule. It's been edited for length and clarity.
I was mostly raised in the small town of Bikaner, Rajasthan, in India, and I always thought I would study medicine until my cousin showed me a video that changed my life.
It was a Code.org video with Mark Zuckerberg, Bill Gates, and other tech rockstars, about how coding is the quickest way to convert an idea into a product. That video was a very big turning point in my life and career.
I'm now part of the Foundation Model main framework team for a major Big Tech company in the US. I recently completed five years with them, during which time I've held four different roles.
I used my master's to move to the US and further my career. But whether or not a higher degree is necessary today is complicated.
There are many roles on machine learning teams
There are multiple rungs on the ladder of Machine Learning teams.
The different roles include researchers, engineers calling on the machine learning models and building applications on top, and the core machine learning people who are developing the actual model itself. Finally, you have the infrastructure stack barrier, doing the product center toolkits to help machine learning teams.
I work on building the foundation of machine learning models, which means I build code that trains software to recognize unseen data and create patterns.
I started my tech career as an intern in India, but knew I had to come to the US to advance
I originally interned at my current company in India. I enjoyed working in India; the work was great, but the core business decisions and figuring out the strategy of the next project were happening at the company headquarters here in the US.
I had no intention of moving to the US earlier. I was quite happy in my country. But overall, I kept feeling like I wasn't doing the best I could in my career because of living so far from the core business decisions, and I decided I wanted to make the move.
I used my internship and master's to get further in AI engineering
I had two ways to go about moving to the US: one was to try to move from within my company, or get a master's. There were two reasons I chose the master's path: the knowledge and extra specialty you can develop through projects and the connections you make.
The biggest thing I took away from my master's program at the University of Wisconsin-Madison was definitely the people.
When I got to the US, I knew a few people at my former company already from my time in India, so I reached out to a bunch of managers directly instead of applying on the job board. I got the interview for the Machine Learning engineering internship quite easily because they were aware of me and my work from before.
Pitching my products internally helped me land a full-time AI engineering role
When I started as an intern again, this time in the US, I did a few things that helped me land this job. I pitched my product to other teams internally to get it adopted. My manager kept telling me that, when they were fighting to get me into the company full time, that was a major thing they used.
Now, as an engineer on the machine learning team, I like to segment my day into three parts. It sort of depends on the project life cycle, but usually, I start by researching. The second part is upstream and downstream check-ins with other team members and clients. I speak with people on other teams, saying, "Hey, this is what we can do, and does this work for you?"
Everyone's favorite part is the third, which is basically hands-on building and coding. I'm lucky enough to spend most of my time being an individual contributor and focusing on coding.
Higher education in tech still matters, but there are other ways
I think it's possible now to skip that education stage. But I have seen a bias in hiring for specific teams, and it's not unbreakable yet.
I was changing countries and cultures, and university was a great way to get through the immigration system and understand the culture. I needed it.
If you want to be in academia and teaching, the higher education path makes sense. But if you want to build something fast, learning and networking can be done in many places. In a city like San Francisco or New York, you could hustle and get the networking benefits of a university and a structured system.
You essentially just need the ability to prove that you can be good at the job. That doesn't really come from a degree. But I find there is usually some bias against applicants without a degree higher than a bachelor's.
Do you have a story to share about AI or higher education bias in tech? Contact this reporter, Agnes Applegate, at [email protected].
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