Software Engineer Machine Learning Interview Questions
Master your tech interview with our curated database of real questions from top companies.
Explain core ML fundamentals
Machine Learning Fundamentals: Regularization, Losses, PCA, and Random Forests Assume standard supervised learning with linear models for regression/c...
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 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...
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...
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...
Cluster city name variants into canonical entities
Normalize City Names for Vote Aggregation Context You have voting records containing a free-text city field. The same city may appear in many forms (e...
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...
Explain optimization and tensor vs pipeline parallelism
Task: Deep Learning Optimization and Parallelism You are asked to explain optimization techniques commonly used to improve deep learning training and ...
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...
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...
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, ...
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...
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...
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...
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...
Compare deep learning framework trends
Deep Learning Framework Trends: PyTorch vs. JAX Prompt Discuss current high-level trends in deep learning frameworks. Then compare PyTorch and JAX acr...
Build and evaluate a Colab classification model
End-to-End Tabular Classification Workflow in Google Colab You are asked to design and implement a complete classification workflow for a tabular data...
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 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...
Explain attention and Transformers
Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT You are interviewing for a software engineer role focused on machine lear...