ML Engineer
Interview Date
April 2022
Result
Not Specified
Difficulty
Hard (Coding Round), Easy/Medium (Technical Interv
Rounds
3 rounds (Coding/Problem-Solving, Technical Interview, Hiring Manager)
Drive Type
Off-Campus / Full-Time
Topics asked
Detailed experience
College: Not Specified (Off-Campus)
Interview Date: April 2022
Interview Type: Off-Campus / Full-Time
Result: Not Specified
Difficulty: Hard (Coding Round), Easy/Medium (Technical Interview DSA)
Rounds: 3 rounds (Coding/Problem-Solving, Technical Interview, Hiring Manager)
Topics Asked: Graph Algorithms, Data Structures (Hash Maps), SQL, Binary Search, Operating Systems, Networking, Projects, Behavioral
The interview process for an ML Engineer consisted of three rounds.
Round 1 - Coding/Problem-Solving (90 minutes): Conducted on the CoderByte platform, this round included two GFG hard/expert level graph questions (a variation of finding the longest path/diameter of a graph and a variation of topological sorting), 13 technical MCQs, and two optional behavioral questions. The candidate successfully solved both coding problems.
Round 2 - Technical Interview (75 minutes): This online round began with introductions. The interviewer then asked the candidate to explain their deep learning summer internship project in detail. Following this, DSA questions were posed (GFG easy/medium level). Questions included: "Wave Sort an array" (candidate optimized to O(N) solution), finding the most frequently occurring word in a .txt file (using a hash map, discussing hash collision mitigating techniques), writing an SQL query for the Nth highest salary, and a Peak finding variation (Binary Search). The interviewer also asked about asynchronous code and parallel programming techniques, Operating Systems concepts like paging and memory management, and explaining the entire flow of a request when hitting a URL (OSI model).
Round 3 - Hiring Manager Round (35 minutes): This short online round involved the Data Science project team manager. After introductions, the manager inquired about the previous rounds and the candidate's overall interview experience. The discussion then focused on the candidate's Machine Learning project, including its features and tech choices. General behavioral questions were also asked.