Ryan Amirkhan, a Machine Learning engineer at Media Distillery, has recently reached his 6-year anniversary with the company. Ryan was one of the company's earliest employees, and his contribution has been vital to its development and success. In this interview, Ryan shares his experiences throughout his transformative journey.
Ryan Amirkhan on his career journey
My name is Ryan, I am 33 years old, born and raised in Amsterdam. I started working at Media Distillery after graduating from MA in Artificial Intelligence at VU. It has been six years now.
I was introduced by a fellow student of mine, he has been working there for a couple of months already. And then I had an interview with the company founders. It was my first full-time job interview, so I was quite nervous.
It was mainly the general vibe of the company. In the past, I had internships at bigger companies, which I didn't really like. I prefer that personal feeling of a startup.
I remember spending a few hours with that fellow student setting up my computer. It was more complicated than I expected. Although I was one of the first employees in the company, for the most part the onboarding process was quite organised. The company was small, so we would have stand-ups for the entire company as one team. We were located at B Amsterdam, a shared office building for startups.
The most noticeable change is that the company grew to around 40 employees now. The entire platform has grown. Back then it was less focused on the deep content use cases. The current products such as EPG Correction Distillery and Topic Distillery weren’t there yet. Another big change is a shift of focus from media monitoring towards video solutions for broadcasters and video operators.
Media Distillery Team. Photo by courtesy of Ryan Amirkhan.
Because the team is relatively small, you don’t have so many colleagues to guide you, so autonomy (one of the company values) occurs naturally.
The values stayed relatively the same. You learn by trying things yourself or working together with someone.
When I started, I was fresh out of university, and had no idea what kind of job I wanted to do as a data scientist. Around my second year in the company I started thinking about my whole job nature. That’s how I evolved into a machine learning engineer: I felt that it was most fitting to my interest and the research I have done so far.
It is kind of a bridge between the prototyping side and productisation side: my personal incentive together with the company direction shaped the overall career. Because every Machine Learning person in the company was either new or had a similar experience to what I had, I had to figure out a lot of things on my own.
The first big achievement was a project involving a face recogniser. We were using an external vendor, while it would be much cheaper and easier to build it internally. I worked on this project with my colleagues.
From building a model and comparing it to the existing solutions until the point where it works, and then deploying it as a service - the entire process is something I really liked, and am very proud of.
I also liked working on detecting topics and chapters, because it's very concrete. On a more technical side, I liked shaping the entire Python side of the company. We didn't really have guidance in that sense, and most of the back end is focused on Java. It was quite a challenge for us. We created a nice foundation, and we are still learning and refining every day.
Company boat trip. Photo by courtesy of Ryan Amirkhan.
My personal goal has always been topics or chapters. I work with various teams towards getting it from the idea to a real product that will be used. It has been difficult, mainly because of the pandemic. The idea itself has been in the company since the media monitoring phase, and I was among the first people to actually work on it.
I always had a belief in the product, and wanted to turn that into a real thing. It was also related to freedom in how you approach things, and general trust in the way of working
One thing I've learned is that making a product takes a long time. At universities, you don’t really think about it. I also learned there is a difference between technology and a product: just because you can build something technically perfect doesn't mean people are willing to pay for it.
On a personal side, I struggled in the past with communicating. Being part of the team helped me to get more comfortable with being quiet, and that it’s okay not to be very talkative. It also helped me to be more vocal when it’s necessary.
I liked the laser tag that we did with the company. It was during my second year. And I also like our Christmas parties.
February 14, 2023
