Eatclubs/box8

Role: SDE

College: Not Specified (On-Campus)

Interview Date: 2025-07-23

Interview Type: On-Campus/Full-Time

Result: Selected

Difficulty: Not Specified (Coding: Easy, Medium, Hard; Technical: Puzzles, High-skilled Maths)

Rounds: 3 rounds

Topics Asked: Coding, Data Structures, Algorithms, Puzzles, Maths, Projects, Machine Learning, System Design (IRCTC website, Rating Algorithm), Backend (ML algorithms in backend)

Experience:

This candidate got selected in the BOX8 on-campus drive. The recruitment process took around 3 days.

Round 1 (Coding Round): This round was held on HackerRank for 90 minutes. It comprised three coding questions: one easy, one medium, and one hard. Resume selection preceded this coding round.

Round 2 (Technical Interview 1): Conducted by a Team Lead and a Software Developer. The interview started with general introductions. A coding question was posed: "Find the square root of a number without using an in-built function", for which the candidate first provided a brute force method and then optimized it using binary search. A puzzle was then asked: "Minimum number of glass required to find the floor from which the glass balls start to break". High-skilled mathematics questions and questions about the candidate's projects were also asked. As the candidate had experience in Machine Learning, the difference between Machine Learning and Coding was discussed.

Round 3 (Technical Interview 2): Also taken by a Team Lead and a Software Developer. This round focused on an in-depth discussion of the candidate's projects, including the motivation and driving force behind them. A coding question involved a "searching problem in a row and column-wise sorted matrix", which the candidate initially solved in O(N log N) and then, with some help, optimized to O(N). System design questions included "how will I design an IRCTC website" and what to expect on the front end. The interviewers also inquired about designing a rating algorithm and ensuring its unbiasedness on the backend side, and how and where Machine Learning algorithms could be used in the backend.