Scale Ai Machine Learning Engineer Interview Questions
Practice the exact questions companies are asking right now.
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"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
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"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Implement multi-head attention and LLM sampling
Task A: Multi-head attention (forward pass) You are implementing a Transformer attention layer. Given: - A sequence length L and model dimension d_mod...
Explain LLM post-training methods and tradeoffs
You are asked about LLM post-training (after pretraining on large corpora). Explain a practical post-training pipeline for turning a base model into a...
Debug ML pipeline and build text parser
- Given raw text files with noisy formatting, implement a robust parser that outputs structured examples; handle delimiters, quoting/escaping, encodin...
Handle customer engagement and manager-rating questions
In a behavioral round focused on customer engagement / leadership principles, you are asked questions like: - “Tell me about a time you worked directl...
Explain Transformers, attention, decoding, RL, and evaluation
Technical Screen: Transformers, Attention, Decoding, RLHF, Evaluation, and Optimization Context: Assume a modern decoder-only LLM unless stated otherw...
Implement universal adversarial attack on GPT-2
You are given a Google Colab notebook and access to a pretrained, aligned GPT-2 language model that has been tuned to avoid generating a small list of...