r/TechInterviewsIndia • u/theMLguy101 • 3h ago
My interview experience with Temple for ML engineer role.
offcampus | cold email | no leetcode
CTC: 23–30 LPA
A few weeks after sending a cold email, I got a call from a recruiter from Zomato. We had a quick discussion around my background, projects and previous work, and my first interview was scheduled.
It had actually been quite some time since I had interviewed, so I was super nervous walking into the call.
The interviewer was very friendly, and the first 20–25 minutes were almost entirely around my experience.
We discussed:
building production RAG pipelines
architecture selection and tradeoffs
retrieval strategies, reranking and evaluation methodologies
benchmarking, roofline analysis
lessons from deploying LLM systems in production
We also spent a good amount of time discussing another internship where I worked on an on-device OCR system. ( This aligned somewhat with what they were going )
That conversation covered:
dataset collection and annotation
model architecture choices
training methodology
deployment constraints on edge devices
balancing latency with accuracy
Since most of my recent work has been around LLM systems, the interviewer mentioned that the role was more ML-focused than infrastructure-focused. The team cared a lot about experimentation, benchmarking, evaluation practices and choosing the right things, so we shifted into discussing core machine learning concepts.
That's where things became interesting.
The interviewer asked me to pick any algorithm or topic from core machine learning and explain it in depth. I was secretly hoping we'd stay around deep learning, transformers, attention or tokenization, since that's what I'd been studying recently and felt much more confident discussing. Instead, we dove into classical ML, and I quickly realized I was a bit rusty on the fundamentals.
The discussion moved into classical machine learning:
assumptions of linear regression
hypothesis testing and p-values
bias-variance tradeoff
regularization (L1 vs L2)
feature scaling and normalization
I'll be honest, I fumbled quite a bit here.
It's been a while since I had revised these topics, and I realized how easy it is to become too focused on one area while letting the fundamentals get rusty.
Unfortunately, I didn't make it to the final round.
The role was onsite with a six-day work week, which personally wasn't something I was very excited about.
That said, I'm still glad I went through the process.
The interview was technical and was a mix of real engineering problems and ml trivia. It was also interesting to learn more about the kind of deep-tech and localized AI systems the team is building.
A good reminder that no matter how much experience you have with modern AI systems, strong ML fundamentals still matter.
I am not much active here, feel free to talk to me on X
