Being an MLE is not complicated
I remember back in 2019, when I was a sophomore in college, I used to think that working in ML meant I had to do research. Naturally, I joined an AI lab on campus and over the next two years I helped in publishing two papers on the intersection of deep reinforcement learning and neuromorphic computing. (check out my technical portfolio on my site to read them!)
Having those publications under my belt was helpful in behavioral interviews and gave me confidence in my foundational DL knowledge, but if I’m being honest, it didn’t directly help me land a career in ML. In fact, even after publishing those two papers, I realized that if I wanted to continue doing research, I would need to pursue a PhD program, then a postdoc, and eventually either join industry or become a professor.
When I really thought about it, the idea of barely getting by financially as I did research—hoping that maybe I’d get a good job after all of that—just wasn’t worth it. Luckily, I found an easier way to start a career in ML.
Becoming a SWE
When my research side quest ended, I joined my peers in pursuing software engineering internships, and luckily, I landed a great role at a big tech company. After graduation, I started working there full-time.
I wasn’t initially working on ML. In fact, I was doing pretty standard full-stack web development—work that any bootcamp graduate could handle with sharp React skills. It was fun to build things, but my desire to work on ML was still there, so I began looking for ways to make it happen.
With a working knowledge of theoretical ML still fresh in my mind, I constantly asked for opportunities to work on ML projects. Fortunately, an ML project finally became a high priority and although it a small-scale project, it taught me that ML is not just about theory. In fact, almost none of my theoretical knowledge from research helped me. Instead, I relied heavily on my foundational software engineering skills: making sound infrastructure decisions, designing scalable data pipeline jobs, and ensuring availability and reliability in our ML service.
MLEs are just SWEs that do some extra things
After working on that ML project, I realized that MLEs are basically SWEs with domain expertise in the various components of ML services—data engineering, modeling, infrastructure, and so on.
Using this new understanding and the project under my belt, I was able to transfer teams internally, becoming a full-fledged MLE at my company. Now, it’s my passion to help others break into ML without being misled into thinking that research is the only path.
My name is Ray, and I want to help as many people as possible work in ML in the easiest way possible and share my insights as I navigate my career. If you like content like this, check out my email newsletter too cuz they heard it first!