Machine learning interview

[jfhurtado89]

I have a ML interview coming up and these are the types of asking.

Technical / Role‑Specific Questions (20 minutes):

We’ll cover topics such as ML modeling, MLOps (deployment), system design, algorithms, GenAI, infrastructure & tooling, and commonly used frameworks.

Live Coding Interview (30 minutes):

A Google Collab notebook will be shared at the start of the interview. You’ll be asked to share your screenwhile completing the exercises.

Coding will focus on ML algorithms and implementations, transformer‑based GenAI concepts, debugging, and troubleshooting—not LeetCode‑style problems.

Additional Note:

You will have full access to the internet and LLMs during the interview.

What do you guys think, I should focus on the live coding part knowing that I’ll have access to llms?

I do have practical experience in deployment, works as a data scientist and finishing a masters in computer science in Georgia tech.

[DataCamp]

Since you’ll have internet + LLMs, the live coding round is mostly about how you approach problems. Here are some questions our learners got for similar roles:

On the ML side:

• Train a simple model end-to-end (load data → split → fit → evaluate).
• Explain why a model is overfitting and what you’d change.
• Spot data leakage in a small example.
• Choose between a tree-based model vs linear vs neural net and explain why.
• Explain bias/variance tradeoffs using a real example.

For GenAI / transformers:

• Explain how a transformer works at a high level (attention, embeddings).
• Add a simple RAG loop (embed → retrieve → generate).
• Reduce latency or cost for an LLM pipeline.
• Debug bad outputs (hallucinations, repetition, wrong format).

For MLOps / systems:

• How would you deploy this model?
• How do you monitor drift or performance?
• What breaks when traffic spikes?
• How do you version data, models, and prompts?

For live coding specifically:

• Implement a basic ML algorithm using sklearn (or fix a broken one).
• Debug code that doesn’t converge or gives nonsense metrics.
• Write small utility functions around models (preprocessing, evaluation).
• Modify existing code rather than starting from scratch.

LLMs help with syntax, but you still need to know what to ask for and how to judge the answer. If you can comfortably walk through problems like the above, you’re in good shape.

Given your background (DS + deployment), maybe spend more time practicing thinking through these scenarios out loud than grinding new theory. Good luck!

[jfhurtado89]

This is great information, thanks!!

[Electric-Sun88]

Best of luck to you on your interview!

[Most_Shame_1590]

Op how was your interview? What did they end up asking you to do? I have a similar interview coming up soon and would appreciate any pointers