The technical screening involved some leetcode style problems and general problem solving + ML questions. The round table was a mix of behavioral questions, statistics, ML, and at least 1 more instance of leetcode problems. Question difficulties ranged from easy, like "how to address in the dataset", to average difficulty questions like "explain the differences between and another" or "can you describe how works.
Tell me about a time when you fail.
Describe your previous work in detail.
How to extract all connected areas in a binary image?
Describe the most exciting/proud project you have ever done.
Five people during "in-house": One algorithms/coding, breadth and depth ML (one each), one ML architecture (recommendation systems), one "leadership principles".
How can you find a unique list of customers who visited on day 1 and then came back for a visit on day 2?
Q: What are the different types of LLMs and when might you use them?
What are the loss function components of single stage detectors?
They have asked various basic questions from machine learning and Deep learning , Recommendation Systems relevant. They also asked two design various models from Recommendation Systems. One round was online coding from basic data structures.
Viewing 81 - 90 interview questions