The phone screen was surprisingly brief, lasting only about 20 minutes but focused heavily on technical concepts. I was asked to design a distributed rate limiter for an LLM inference API, which I had practiced on PracHub during my prep. The technical rounds that followed were more in-depth, delving into system design and behavioral questions, but the earlier prep really paid off. In the end, I received an offer, which I decided to decline as I felt it wasn’t the right fit for me.
Interview questions [1]
Question 1
Design a distributed rate limiter for an LLM inference API
The process is intense and highly focused on practical engineering rather than just theoretical algorithms. After the initial recruiter screen and a technical phone screen, there was a heavy emphasis on a "Learning Sample" or a take-home style project that you then have to defend in a deep-dive session. They want to see how you handle real-world system bottlenecks, concurrency, and large-scale data ingestion.
Interview questions [1]
Question 1
One part involved optimizing a distributed system mockup where I had to identify a race condition under high load. It wasn't about memorizing a textbook answer; it was about live debugging and showing your thought process in a production-like environment.
The interview process took a few weeks in total. First there was an initial phone screening, then two technical interviews, and then a final day with 3 interviews. Entire process felt smooth and professional
Interview questions [1]
Question 1
Final round had behavioral, project presentation, and general leetcode-esque question
