Gerry Fernando Patia leads AI infrastructure projects at Robinhood, an online platform for investors. Having emigrated from Indonesia and attended a lesser-known U.S. university, he didn’t find it easy to break into AI. He spoke to us about how he overcame hardship and landed his dream job.
Name: Gerry Fernando Patia
Title: Machine Learning Engineer, Robinhood
Location: San Francisco Bay Area
Education: Bachelor’s of Computer Science, California State Polytechnic University-Pomona
Favorite machine learning discipline: Natural language processing
What are your core responsibilities at Robinhood?
I am a machine learning engineer in charge of building the centralized machine learning infrastructure to support other internal teams. For example, data scientists who train and implement models for production. My team and I work closely with our partner teams throughout the organization to enable broader access to machine learning technology and help other developers be more efficient.
What does a normal workday look like?
I start my day at 9 a.m. with a team stand-up followed by a number of meetings for my various projects and cross-functional responsibilities. I usually have opportunities to code sparingly in between meetings. However, I do most of my coding in the afternoon. During my coding sessions, I jump between writing code for my current projects, reviewing my team’s code, or designing new systems. I usually wrap up my day around 5 p.m. and head to soccer practice.
How does your role as a machine learning engineer differ from that of a data scientist?
As a machine learning engineer, one of my responsibilities is to help our data scientists build models more efficiently. The data scientist is usually the one in charge of building models that have a direct impact on the product. Whereas a machine learning engineer focuses on building the machine learning infrastructure.
Are you working on any cool projects you can tell us about?
One of the main ways we use machine learning at Robinhood is to catch fraudsters on our platform. Our models can predict bad behavior before it harms our users. It is really cool to see how machine learning is playing such an important role in maintaining the privacy and safety of fintech.
How did you get interested in AI? What was your first exposure to this world, and what was it that made you want to pursue it as a career?
My first exposure to AI was through Hollywood movies like I, Robot with Will Smith and Mission Impossible with Tom Cruise. Unfortunately, when I was in college, I did not have many opportunities to learn about AI. Instead, I decided to research online and discovered Andrew Ng’s Machine Learning Specialization.
What were some of the most formative courses you took along the path to becoming an AI professional?
The machine learning specialization was the only coursework I took before joining Facebook as a machine learning engineer, which was my first job out of college. This course gave me a strong foundation on theoretical machine learning.
Machine learning engineer at Facebook is a great job to get first thing out of college. What was your job search and interview strategy?
It was very difficult for me to find a job at first. I didn’t graduate from an Ivy League school, nor did I have strong work experience. On top of that, many smaller companies aren’t willing to hire international students. I became aware of these disadvantages as I neared graduation. I had to become proactive to build up my portfolio.
I actively increased my knowledge outside the classroom by taking online classes and competing in national hackathons such as HackHarvard, HackTech and HackBrown. I won several of these hackathons, and that put me on the map with recruiters. This opened up the doors to get interviews with companies.
Do you have any dos and don’ts to share with other people who hope to land jobs at prominent tech companies?
For students, don’t just focus on your school work. The subjects at school are meant to shape your foundational understanding. But they often aren’t practical enough to apply to real life. Be proactive to get practical skills outside of school. You can push yourself to learn new skills that are relevant to tech companies by doing projects on your own. Contributing to open source projects can be a great alternative. Also, make sure you prepare ahead of time. I had a good reputation with recruiters due to my performance in hackathons, but I still prepared by doing a lot of practice questions ahead of my interview.
You grew up in Indonesia and came to the U.S. for college. Did you experience any difficulties with this move?
Yes. As an international student, I struggled to find a job due to the language barrier, visa restraints, and other difficulties. The strategy I shared above — participating in notable hackathons — helped me to build my reputation until I became qualified for my dream job at Facebook.
Do you have advice for people who want to move from abroad to the U.S., either for school or work?
The most difficult thing for me was trying to adapt to American culture while being far from my family and friends back home. For those who are trying to move abroad to the U.S., I recommend that you plan to find a community that resonates with you. This community will help you to sustain your own sense of self-worth while working towards your career goals. For me it was soccer. Finding a soccer club made my transition to life in the U.S. much easier.
How do you keep learning?
Every week, I research a topic or company that I am curious about, such as blockchain, ChatGPT, and so on. I am also getting into the habit of reading more, learning about spaces outside of programming or tech, and expanding my knowledge more holistically.
We are privileged to live in a world with so many learning resources. Find the best medium that works for you and carve out time to invest in yourself!