Microsoft SDE Intern Interview Experience
💼 LTIMindtree Interview Experience (On-Campus) | Fresher | 2026
Salesforce SMTS | Interview Experience | Rejected
JPMC | SDE2 (Associate) - Java Backend - Interview Experience + Compensation
Microsoft - SDE2 - Coding Round
Amazon | Interview (1st Round) | Bengaluru | Applied Data Scientist
Summary
I recently had my first online interview at Amazon’s Bengaluru office for an Applied Data Scientist role, focusing on theoretical understanding of LLMs, transformer internals, and general ML/statistics concepts. A further round focusing on coding and resume projects is scheduled.
Full Experience
Interview Experience
I recently had my first online interview at Amazon’s Bengaluru office recently in May 2025. I have 4 years of industry experience as a Data Scientist in a mid sized company. The round was focused on theoretical understanding of large language models (LLMs), transformer internals, and general ML/statistics concepts.
Update
Further round scheduled which would focus on the coding aspects alongwith deep dive into resume projects.
Interview Questions (4)
I was asked to explain the concept of QKV (Query, Key, Value) in the attention mechanism, and specifically how it’s implemented and used differently in BERT vs GPT.
Follow-up: Why does GPT only use causal masking? What architectural changes exist in decoder-only vs encoder-decoder models?
They wanted to check my statistical intuition. I was asked to define a point estimator, and explain the bias-variance tradeoff with relevant examples.
Follow-up: What would you prefer in a low-data regime: high bias or high variance model?
What are the key sources of stochastic behavior in LLMs during inference?
Follow-up: How does temperature sampling and top-k/top-p affect generation?
This was more of a conceptual discussion. I was asked:
“How do LLMs generalize well with very little task-specific data during fine-tuning or prompting?”
Follow-up: Why doesn’t the classical bias-variance limitation seem to apply here?