Major questions were related to my project which was computer vision/DL based. 1. What are the difficulties I faced in my project and how I counteracted it? 2. Explain U-Net and what novel thing it offers? 3. Explain regularization and it's different types? 4. How KL divergence loss is different from cross entropy loss?
In the DSA I was asked to coding questions and 4-5 questions on implementation of some data structures The ML round involved discussion on projects mentioned in resume and required in depth knowledge of the key concepts
Bar‑Raiser: A lot of examples for the leadership principles. Breadth: ML concepts Depth: Statistics, which was not expected Coding; ML + LC code Application: group-related problem
can you give me an example of self learning.
- Technical questions on Transformer architectures and their asymptotic complexity - Behavioral questions (e.g. "describe a time you had to innovate") - Algorithmic questions (leetcode level easy-medium)
NLP, ML fundamentals, model deployment, etc.
Research experience about RL and LLM
If you will be a tech lead to develop an LLM, which architecture you will choose? (i.e., decoder-only or encoder-decoder) and why?
Coding: find median from data stream
ML design, AI and ML basics
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