Round 1 – Technical Interview

Duration: ~40 minutes

This round focused on problem-solving, research experience, and core ML concepts.

DSA Question

One greedy problem from the Codeforces platform. I discussed the approach, wrote the solution, and explained the time and space complexity.

Research and Project-Based Questions

Questions related to the research paper I wrote in my first year that was accepted at a conference

➜ What tech stack was used?

➜ Why was this approach preferred over others?

➜ What dataset was used and how was it preprocessed?

Questions on my LLM project

➜ Why did you use the DistilBERT model?

➜ How is DistilBERT different from BERT?

➜ What is the architecture of DistilBERT?

➜ What was the use case for this model?

Statistics Questions

➜ What are outliers?

➜ How do you detect outliers?

➜ How do you remove outliers?

➜ A scenario-based question related to handling outliers in a real dataset

After this round, I received a call within 20 minutes informing me that the next round would be scheduled in one hour.

Round 2 – Advanced Technical Interview

Duration: ~40 minutes

This round focused on logical reasoning, ML fundamentals, and core computer science concepts.

Puzzle Question

➜ A logic puzzle involving bottles and prisoners

➜ I initially tried solving it using a binary search approach and later discussed a bit manipulation-based solution.

Internship Experience Discussion

Detailed discussion about my previous research internship at Carnegie Mellon University

➜ Type of data used

➜ Nature of the work I did

➜ My individual contributions

Core Computer Science and ML Questions

➜ Object-Oriented Programming concepts

➜ What is linear regression?

➜ What is logistic regression?

➜ Equations for linear and logistic regression

➜ Assumptions of linear regression

LLM and Deep Learning Questions

➜ What are Large Language Models (LLMs)?

➜ What are transformers?

➜ Explain the architecture of transformers

➜ How does the attention mechanism work?

The round concluded with a short discussion about the role, responsibilities, and perks offered by the company.

Round 3 – Managerial and HR Round

This was the final round and was largely managerial in nature.

HR and Behavioral Questions

➜ What are your expectations from this internship?

➜ Why do you want to work here?

➜ By when can you join and until when can you continue?

➜ What is your passion and long-term goal?

➜ Why did you choose AI-focused internships?

➜ How will you manage work commitments alongside academics?

Final Outcome

Two days after the final round, I received confirmation that I had been selected. By the evening, I received the offer letter for the AI Engineering Intern role.

Key Takeaways

The interview process was holistic and well-balanced. It covered DSA, puzzles, machine learning fundamentals, LLM architecture, research understanding, and behavioral aspects. Strong clarity on projects, the ability to explain decisions, and comfort with fundamentals played a major role in clearing the interviews.

Final Thoughts

This experience showed that AI internship interviews are not limited to model training or coding alone. They test depth of understanding, reasoning ability, and communication skills. Being confident about your work, staying calm, and thinking aloud during problem-solving can significantly improve your chances.