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 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...
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...
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...
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 ...
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...
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...
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...
Explain key ML metrics and techniques
You are asked a set of short conceptual machine learning questions. 1. Confusion matrix and metrics For a binary classification problem: - Def...
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...
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 DPO and construct its training data
You are working on a project to fine-tune a large language model (LLM) using Direct Preference Optimization (DPO). Answer the following: 1. Conceptual...
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...
Discuss overfitting, contrastive learning, transformers
You are interviewing for an applied scientist role and are asked several theory questions. 1. Overfitting - Define overfitting and underfitting in ...
Explain prompt engineering strategies for chatbots
Prompt Engineering for Reliable, Steerable, and Safe Chatbots Context You are designing a production-grade chatbot that must be reliable (consistent, ...
Compare bagging, boosting, random forests, and bias-variance
You are asked several ML theory questions: 1. Bagging vs. boosting - What is the difference between bagging and boosting? - When would you prefe...
Explain RL policy types and modern policy gradients
Machine Learning Fundamentals (RL + Attention) Part A — Reinforcement Learning 1. Define on-policy vs off-policy learning. - What makes an algorith...
Discuss large language models
LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation Context You are interviewing for a Software Engineer role focused on m...
Compare ML frameworks and trends
ML Framework Trends and PyTorch vs. JAX Differences Context You are in a technical screen for a software engineer (machine learning systems) role. Ans...