Software Engineer Machine Learning Interview Questions
Practice 50 real Machine Learning interview questions for Software Engineer roles. From companies including Amazon, NVIDIA, OpenAI, Microsoft, Google.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"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."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"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."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"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."
Use a Fitted Line to Predict a Future Data Point
Use a Fitted Line to Predict a Future Data Point You receive observed points (x_i, y_i) and need to predict y for a future input x_future. Assume a pr...
Accelerate Tree-Model Inference
Accelerate Tree-Model Inference You must reduce inference latency and raise throughput for a deployed boosted-tree model without changing its predicti...
LLM Foundations: Architecture, Adaptation, and Steering
This question evaluates a candidate's conceptual understanding of large language model architecture, adaptation, and inference-time control. It probes...
Debug a GRPO training loop and explain ratios
You are given a simplified implementation of a GRPO (Group Relative Policy Optimization) training step for an RLHF-style policy model. The training is...
Present and Defend Recent Research on AI Agents
Prepare and defend a research presentation about one recent project, then connect its lessons to AI-agent systems. Use your own work; do not invent re...
Implement and Debug Backprop in NumPy
Two-Layer Neural Network: Backpropagation and Gradient Check (NumPy) You are implementing a fully connected two-layer neural network for multi-class c...
Implement and derive backprop from scratch
Tiny Neural Network From First Principles: Binary Classification Implement and analyze a minimal neural network for binary classification with a singl...
Design sequence decoding with greedy and beam search
Design sequence decoding with greedy and beam search Next-Token Decoding: Greedy and Beam Search Context You are given a probabilistic next-token dict...
When should products use AI?
A product-oriented interview asks you to discuss AI adoption in software products. Explain how you would decide whether a feature should use AI or a t...
Debug a failing ML classifier
Debugging a Churn Prediction Pipeline With Poor Generalization Context You have inherited a binary churn prediction system. The goal is to predict whe...
How to Identify Best Battery Group
You have historical quality data for batteries that were randomly assigned to one of 5 groups. Each battery then goes through 3 different quality test...
Explain LLM fine-tuning and generative models
This question evaluates understanding of LLM adaptation techniques and trade-offs (fine-tuning and parameter-efficient methods) alongside knowledge of...
Explain Common Machine Learning Tradeoffs
The interview included a rapid-fire machine learning theory round. Be prepared to answer questions such as: - What are overfitting and underfitting? H...
Evaluate Linear Regression Assumptions and Fit Three Points
Evaluate Linear Regression Assumptions and Fit Three Points Discuss the assumptions behind ordinary least squares, how multicollinearity affects a fit...
Implement and analyze custom attention
Implement Scaled Dot-Product Attention in PyTorch (from scratch) Context You will implement a numerically stable, vectorized scaled dot-product attent...
Implement multi-head self-attention correctly
Implement Multi-Head Self-Attention (from scratch) Context You are given an input tensor X with shape (batch_size, seq_len, d_model). Implement a mult...
Choose models for trading tasks
This question evaluates a candidate's competency in selecting and reasoning about machine learning models for quantitative trading and pricing, includ...
Explain and Compare SGD and Adam
Explain and Compare SGD and Adam Explain how stochastic gradient descent updates model parameters and how Adam modifies those updates using moving est...
How would you model stock price prediction?
This question evaluates competency in applying machine learning to financial time-series, covering target definition, data selection, feature engineer...
Explain normalization, regularization, CTR, imbalance handling
This question evaluates mastery of normalization methods, regularization techniques, click-through-rate modeling, and class-imbalance strategies, with...