My MLE Interview Prep Journey: What Actually Worked After spending two months intensively preparing for MLE interviews, I wanted to share what actually helped me succeed. This isn't another generic guide - these are the specific strategies and resources that made a real difference. The Reality Check Let me be honest - preparing for MLE interviews while job hunting is exhausting. You're juggling coding practice, ML theory, system design, and trying to keep your sanity. Here's what I learned the hard way and what actually moved the needle. --- Coding Interviews: It's Not Just About Solving Problems The Game Changer: Mock Interviews I cannot stress this enough - do mock interviews every single day. I used: - Interviewing.io - The anonymous aspect helped with nerves - Pramp - Free and surprisingly good quality At first, I bombed these mocks spectacularly. But after about 20 sessions, something clicked. I learned that most interviewers actually want to help you succeed. What Nobody Tells You The biggest revelation: When you're stuck, just walk through a concrete example out loud. I'd say something like "Ok, let me trace through this with input [1,2,3]..." and 8 times out of 10, the interviewer would jump in with a hint. Also, ML coding interviews are way more forgiving than LeetCode style ones. I've had interviewers literally say "Just Google the NumPy syntax, I don't care about that." Resources That Actually Helped - Deep-ML.com - Practical ML coding problems - Perplexity.ai - My secret weapon for quickly understanding concepts --- ML Knowledge: You Can't BS Your Way Through This The Mock Interview Hack Here's my dirty secret: I learned more ML theory from bombing mock interviews than from any textbook. My process: 1. Do a mock interview 2. Get destroyed on some concept 3. Immediately afterward, research it thoroughly using Perplexity 4. Write it down in my own words 5. Explain it to my rubber duck (yes, really) TryExponent has ML-specific mocks that were gold for this. Topics That Came Up Constantly - Bias-variance tradeoff (know this cold) - Regularization techniques (especially L1 vs L2) - Evaluation metrics for imbalanced data - Gradient descent variants - When to use different models and why --- ML System Design: The Part That Scared Me Most The Resources That Saved Me Shusen Wang's YouTube channel - Absolute goldmine. I watched his videos during lunch, re-watched them during workouts. His explanations just make sense. Two books that are actually readable (not academic paper dense): 1. "Machine Learning System Design Interview" by Ali Aminian and Alex Xu 2. "Generative AI System Design Interview" by Ali Aminian and Hao Sheng I read these cover to cover, took notes, and practiced drawing system diagrams until my tablet ran out of storage. What They Actually Ask Forget the fancy stuff. Most companies asked about: - Recommendation systems (everyone asks this) - Search ranking - Fraud detection - How to scale model serving The key is being able to discuss trade-offs. They don't want perfection; they want to see you think through problems. --- Behavioral Interviews: Be Human, Not a Robot My Approach I prepared about 10 stories using STAR format, but here's the twist - make it a conversation, not a presentation. I learned to: - Pause and ask "Does that make sense?" or "Would you like me to elaborate?" - Be genuinely humble - "My teammate actually came up with the clever part..." - Show I learned from failures - "Looking back, I should have..." The best advice I got: They're trying to figure out if they want to work with you. Be someone they'd want to grab coffee with. --- The Tools That 10x'd My Efficiency For Research Perplexity.ai and Google Deep Research - These saved me hours. Instead of digging through papers, I'd get clear explanations instantly. For Reading Papers/Blogs Immersive Translate - This browser extension shows translations alongside English text. As a non-native speaker, this literally doubled my reading speed. For Retention NotebookLM - This blew my mind. It converts articles into conversational podcasts. I'd listen during runs and the concepts actually stuck. --- Real Talk: What Interview Day Looks Like The Routine That Worked - Wake up 2 hours before the interview - Light exercise (just 15 mins to calm nerves) - Review my "cheat sheet" of key concepts - Do one easy LeetCode to warm up - Deep breathing exercises (sounds silly, works great) During the Interview - Have water nearby - Use a proper headset (AirPods died on me once - never again) - Keep a notepad for drawing diagrams - If you don't know something, just say it - then explain how you'd figure it out --- The Mindset Stuff Nobody Talks About Dealing with Rejection I got rejected. A lot. What helped: - Treating each interview as practice for the next one - Taking notes on what went wrong immediately after - Having a "rejection ritual" - mine was a long walk and good coffee - Remembering that it's a numbers game Staying Sane - I set a r