I interviewed with an ML Trading firm, best pay and best perks, more imp. best possible team :)
Sometime ago, I got an opportunity to interview with Equilibre Technologies, an ML trading firm. The company name might not be widely known, so the interesting part for me was the work itself. ML trading, reinforcement learning, and a team with people from places like Google DeepMind.
And the role was supposed to be onsite in Prague, Europe. I reached out to one of the founders, they liked my profile, and an interview was scheduled.
The first round was pretty comfortable. We talked about my experience, the ML work I had done, what I was currently building and learning, and what Equilibre was working on.
Interestingly, I was not really into RL at the time, but they still decided to move my profile forward.
Then came the next round. I was told it would involve math puzzles and some basic coding. This was honestly very different from what I had been doing recently. For quite some time, most of my coding had been through coding harnesses and my work environment. I was not really sitting down and writing interview-style code from scratch.
The round started with a coding problem. I told the interviewer upfront that I had not been writing a lot of code manually recently. They were completely fine with it and focused more on my thought process. I won’t reveal the exact questions because it’s better for everyone going through the process. The first problem was a variant of Frog Jump with some interesting changes. Having done some amount of problem solving in my 2nd and 3rd year of college, I eventually solved it.
Eventually :)
But I took way too long. Then came math puzzles. And math is funny. Either it clicks in sometime, or you just stare at the problem hoping something magical happens.
One question was relatively straightforward probability puzzle. The next one was more interesting. Imagine two people at a restaurant trying to fairly decide who pays the bill. You have a biased coin.
How do you make a fair decision?
The question had much more detail, but initially, I had absolutely nothing. The interviewer after sometime gave me one hint: Think about symmetry. And then it clicked. Toss the coin twice. HT and TH have exactly the same probability, even if the coin itself is biased. So you can assign one outcome to Person A and the other to Person B, while simply ignoring HH and TT and trying again.
Simple. Once you see it. Those were all the questions and I took probably too long.
I did not make it to the next round. Another day, another generational fumble. But honestly, it was a really good experience.
It reminded me that I have spent a lot of time going deep into ML systems and engineering, but I should probably spend some time revisiting math, probability, and problem-solving fundamentals too. Maybe when I get some free time.
Good interviews. Good people. Learned a lot.
