Software Engineer Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
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Implement and Debug Backprop in NumPy
Two-Layer Neural Network: Backpropagation and Gradient Check (NumPy) Context You are implementing a fully connected two-layer neural network for multi...
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...
Build a Heart Disease Baseline
You are given a tabular dataset for predicting whether a patient has heart disease. The dataset contains a binary target column such as has_heart_dise...
Debug a Machine Learning Pipeline
Debugging a Sudden Accuracy Drop in a Deployed ML Pipeline Context You are on-call for a production machine learning service. Monitoring alerts show t...
Implement and derive backprop from scratch
Tiny Neural Network (From First Principles): Binary Classification Context You will implement and analyze a minimal neural network for binary classifi...
Explain overfitting, regularization, and LLM techniques
You’re in an ML interview. Answer the following conceptual questions clearly and concisely, using examples where helpful: 1) Model fit - What is overf...
Explain LLM fine-tuning and generative models
Machine Learning fundamentals (LLM / Generative AI track) You are interviewed for an ML role focused on LLMs and generative AI. Part A — LLM fine-tuni...
Debug a failing ML classifier
Debugging a Churn Prediction Pipeline With Poor Generalization Context You are evaluating a binary churn prediction system with: - Training ROC AUC: 0...
Explain normalization, regularization, CTR, imbalance handling
You are interviewing for an applied ML role. Answer the following fundamentals clearly and concretely (you may use equations and practical examples): ...
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...
Design sequence decoding with greedy and beam search
Next-Token Decoding: Greedy and Beam Search Context You are given a probabilistic next-token dictionary D that maps each token t to a dictionary of ca...
How would you model stock price prediction?
Scenario You are asked to use machine learning to predict stock prices (or more realistically, predict future returns / price direction) for a trading...
Derive MLP shapes and explain PyTorch broadcasting
You are given a standard MLP layer (fully connected layer) used in deep learning. 1. Write the forward computation for a linear layer with bias. 2. Gi...
Find minimum of unknown convex function
You are given access to an unknown univariate convex function \(f(x)\) defined on a closed interval \([L, R]\) on the real line. - You cannot see the ...
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...
Explain learning paradigms, loss, and embeddings
ML fundamentals (oral) Answer the following conceptual questions clearly and with examples: 1. What is supervised learning? What are typical inputs/la...
Implement AUC-ROC, softmax, and logistic regression
You are asked to implement a few core ML building blocks from scratch (no ML libraries such as scikit-learn). You may use basic numeric operations and...
Build a bigram next-word predictor with weighted sampling
You are given a training set of token sequences (sentences), for example: ` [["a","b","c"], ["a","s","d"]] ` 1) Train a simple next-word prediction m...
Explain Transformers and QKV matrices
Transformer Self-Attention: Q, K, V, Multi-Head, and Positional Encoding Context: You are given a sequence of token embeddings X (length n, model dime...
How would you evaluate an AI feature?
You’re building an AI-powered feature (e.g., an AI assistant or AI-enhanced search). Interviewers ask: “How do you measure results and compare metrics...