After submitting my resume, the interview process began within a few days.
Phone Screen: A technical interview focused on finding duplicate files. The interviewer asked detailed questions about chunk size, hashing algorithms, and how to identify I/O vs. CPU bottlenecks.
Virtual Onsite: This included six rounds.
Coding: A problem on multi-threaded web crawlers. The key was implementing concurrency and addressing distributed crawling across multiple machines.
Technical Project & Hiring Manager: These rounds were a deep dive into my past projects and behavioral questions.
Culture Interview: Focused on AI safety and behavioral questions, with detailed, probing discussions.
System Design: A classic Anthropic question on designing a solution for an efficient GPU inference API.
Additional Coding: I was given a new coding challenge after my initial performance was deemed "inconclusive." The task was to implement a Python LRU cache that handles `args` and `kwargs`, with a follow-up on making it persistent.
The overall process was fast-paced, and while the problems weren't overly difficult, they required a strong understanding of underlying principles to handle follow-up questions effectively.
Phone Screen: A technical interview focused on finding duplicate files. The interviewer asked detailed questions about chunk size, hashing algorithms, and how to identify I/O vs. CPU bottlenecks.
Virtual Onsite: This included six rounds. Coding: A problem on multi-threaded web crawlers. The key was implementing concurrency and addressing distributed crawling across multiple machines. Technical Project & Hiring Manager: These rounds were a deep dive into my past projects and behavioral questions. Culture Interview: Focused on AI safety and behavioral questions, with detailed, probing discussions. System Design: A classic Anthropic question on designing a solution for an efficient GPU inference API. Additional Coding: I was given a new coding challenge after my initial performance was deemed "inconclusive." The task was to implement a Python LRU cache that handles `args` and `kwargs`, with a follow-up on making it persistent.
The overall process was fast-paced, and while the problems weren't overly difficult, they required a strong understanding of underlying principles to handle follow-up questions effectively.