Machine Learning Interview Questions

Showing 331–360 of 572 results

• NVIDIAMedium·Software Engineer
  Derive MLP shapes and explain PyTorch broadcasting

  This question tests the ability to calculate matrix transformation dimensions in a Multilayer Perceptron (MLP) and explains the rules governing PyTorch broadcasting when performing operations between tensors of different shapes.

  Machine LearningTechnical ScreenPyTorchLinear AlgebraTensors
• NVIDIAHard·Data Scientist
  Diagnose overfitting, DenseNet, preprocessing, CV

  This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.

  Machine LearningHR Screen
• NVIDIAMedium·Software Engineer
  Explain Transformers and QKV matrices

  This question evaluates conceptual and mathematical understanding of the Transformer self-attention mechanism, including how query, key, and value matrices are derived and used. It assesses depth of knowledge in modern deep learning architectures, commonly asked in machine learning and software engineering interviews to test reasoning about sequence modeling and attention-based design.

  Machine LearningTechnical Screen
• NVIDIAMedium·Machine Learning Engineer
  Explain bias-variance, calibration, and model drift

  This question tests your conceptual understanding of model performance degradation over time, the significance of calibrated outputs for decision-making, and the fundamental trade-off between bias and variance.

  Machine LearningTechnical ScreenModel EvaluationProduction MLBias-Variance
• NVIDIAHard·Software Engineer
  Explain optimization and tensor vs pipeline parallelism

  This question evaluates knowledge of deep learning optimization techniques (quantization, pruning, knowledge distillation, kernel/operator fusion, memory and throughput optimizations) and model parallelism strategies (tensor versus pipeline), measuring competency in performance engineering, scalability, communication patterns and system-level trade-offs for training and inference. It is commonly asked to assess an engineer's ability to reason about practical and conceptual trade-offs, collectives, bottlenecks and mitigations in distributed and resource-constrained environments, and it falls under the Machine Learning/Deep Learning domain requiring both conceptual understanding and practical application.

  Machine LearningTechnical Screen
• NNatooraMedium·Data Analyst
  When use LLMs for reporting?

  This question assesses the architectural trade-offs, reliability concerns, and suitability of using LLMs for automated business reporting tasks compared to traditional deterministic methods.

  Machine LearningTechnical ScreenLLMRAGReporting
• NNatooraMedium·Data Analyst
  Why use LLMs for daily summaries?

  This question evaluates a candidate's ability to justify tooling and architecture choices for automated daily summaries, comparing LLM-based natural language generation with rules-based or templated alternatives while accounting for data sources, consumer intent, validation, failure modes, and cost/latency trade-offs.

  Machine LearningTechnical Screen
• NetflixMedium·Data Scientist
  Address Fraud Detection with Imbalance and Concept Drift Solutions

  Evaluates end-to-end fraud detection ML system design for online payments under drift and imbalance. Strong answers handle delayed labels, class imbalance, feature engineering, cost-sensitive validation, real-time deployment, threshold actions, monitoring, retraining, and concept drift.

  Machine LearningOnsite
• NetflixMedium·Machine Learning Engineer
  Compare Losses and Explain LoRA

  This question evaluates understanding of loss functions (mean squared error versus cross-entropy) and parameter-efficient fine-tuning via Low-Rank Adaptation (LoRA), emphasizing probabilistic interpretations, optimization behavior, and low-rank parameterization.

  Machine LearningTechnical Screen
• NetflixHard·Data Scientist
  Design a robust conversion propensity model

  This question evaluates a candidate's competency in end-to-end propensity modeling, covering correct labeling to avoid post-treatment leakage, time-windowed feature engineering, class-imbalance and calibration strategies, causal uplift estimation, and production concerns such as monitoring, serving consistency, and cold-start scoring.

  Machine LearningOnsite
• NetflixMedium·Machine Learning Engineer
  Explain self-attention, LoRA, Adam vs SGD, ViT

  This question evaluates understanding of modern Machine Learning/Deep Learning topics, including self-attention mechanics (queries, keys, values and scaled logits), Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning and memory savings, optimizer behavior (Adam versus SGD with momentum), and architectural trade-offs between Vision Transformers and CNNs including patch-size considerations. It is categorized under Machine Learning and is commonly asked because it probes both conceptual understanding and practical application—testing reasoning about training dynamics, model scaling, fine-tuning strategies, and resource/performance trade-offs.

  Machine LearningTechnical Screen
• NetflixMedium·Machine Learning Engineer
  Explain tokenization and Transformer variants

  This question evaluates understanding of tokenization techniques and Transformer architecture, covering competencies in subword and SentencePiece-style tokenizers, Transformer block internals, and comparisons of modern architectural variants and trade-offs.

  Machine LearningTechnical Screen
• NetflixHard·Machine Learning Engineer
  L1 vs L2 Normalization: Definitions, Geometry and When to Use Each

  A machine learning fundamentals question asking how L1 normalization differs from L2 normalization of a vector. It tests precise definitions, what each normalized vector represents geometrically, when each is the right choice, and the ability to keep normalization distinct from L1 and L2 regularization.

  Machine LearningTechnical Screen
• NetflixEasy·Machine Learning Engineer
  ML Basics Check: Bayes' Rule, Forward-Pass Arithmetic, KNN and Transformer Concepts

  A set of basic machine learning checks: a Bayes' rule posterior with a low base rate, forward-pass and parameter-count arithmetic for small neural networks, and true-or-false concept checks on k-nearest neighbors and Transformer attention. It tests quick, accurate reasoning on core ML fundamentals.

  Machine LearningOnline Assessment
• NNewsBreakMedium·Machine Learning Engineer
  Derive and implement calibration via temperature scaling

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Derive and implement calibration via temperature scaling states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• NNewsBreakHard·Machine Learning Engineer
  Design approach for class imbalance

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design approach for class imbalance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• NNewsBreakMedium·Software Engineer
  Find Optimal Piecewise Constant Regression Parameters

  This problem tests the ability to derive optimal constants for a piecewise regression model with a single split point and to implement an efficient search algorithm to minimize the sum of squared errors.

  Machine LearningTechnical ScreenRegressionOptimizationPrefix Sums
• NextdoorMedium·Machine Learning Engineer
  Build a model using only pandas/numpy

  This question evaluates proficiency in applied machine learning competencies including data preprocessing, categorical encoding, feature scaling, implementing baseline models and optimization using numpy/pandas, and selecting appropriate evaluation metrics.

  Machine LearningTechnical Screen
• NNikeMedium·Software Engineer
  Write Pseudocode for Embedding Creation and Vector Search

  This problem tests the ability to design a robust production pipeline for generating text embeddings and performing efficient, metadata-aware vector retrieval.

  Machine LearningTechnical ScreenEmbeddingsVector DBData Pipelines
• OneMain FinancialEasy·Data Scientist
  Explain decision trees and tree ensembles

  This question evaluates understanding of decision trees, splitting criteria for classification versus regression, hyperparameter effects on the bias–variance trade-off, and the mechanics and rationale behind tree-based ensemble methods in the Machine Learning domain.

  Machine LearningTechnical Screen
• OneMain FinancialHard·Data Scientist
  Select and tune XGBoost hyperparameters

  This question evaluates skills in selecting and tuning XGBoost hyperparameters, managing severe class imbalance and sparse one‑hot encodings, handling missing values, and designing compute‑efficient training and grouped cross‑validation to prevent user‑level leakage.

  Machine LearningTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Build and troubleshoot image classification and backprop

  Build and troubleshoot image classification and backprop evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Compute Matrix Prefix Products And Gradients

  This question evaluates understanding of automatic differentiation for matrix operations, specifically forward and reverse-mode gradients for sequential, associative (but noncommutative) matrix products, together with algorithmic analysis of time and space complexity.

  Machine LearningOnsite
• OpenAIMedium·Machine Learning Engineer
  Compute entropy and implement 1-NN

  This question evaluates numerical stability and streaming computation for entropy estimation from logits, along with efficient vectorized linear-algebra implementation of 1-nearest-neighbor inference and its interpretation as a neural-network forward pass under different distance metrics.

  Machine LearningOnsite
• OpenAIMedium·Machine Learning Engineer
  Debug MiniGPT and Backpropagate Matmul

  This question evaluates proficiency in debugging PyTorch transformer implementations, reasoning about attention mechanics and causal masking, implementing key–value caching for autoregressive decoding, and constructing provably correct backward passes for matrix multiplications and custom autograd.Functions.

  Machine LearningTechnical Screen
• OpenAIMedium·Machine Learning Engineer
  Debug Transformer and Add KV Cache

  This problem tests your understanding of transformer architecture by requiring you to fix a broken attention mechanism and implement a Key-Value cache to optimize autoregressive inference.

  Machine LearningOnsiteTransformersAttentionInference
• OpenAIMedium·Software Engineer
  Debug a Machine Learning Pipeline

  This question evaluates a candidate's ability to diagnose production machine learning failures, covering competencies in data quality, data and concept drift detection, model versioning and deployment checks, and operational debugging within an MLOps context.

  Machine LearningTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Debug a Small PyTorch Transformer with Four Bugs, Then Add a KV Cache

  Debug a small PyTorch transformer language model that hides four bugs, some of which crash and some of which silently ruin training, then add a key-value cache so generation computes only the newest token each step. It tests causal masking, positional encoding, training-loop order and cached decoding.

  Machine LearningOnsite
• OpenAIMedium·Machine Learning Engineer
  Debug a Transformer Implementation and Implement a KV Cache for Decoding

  Practice ML coding question on Transformer language models: find and fix several deliberately planted bugs in a decoder-only Transformer implementation, then implement key-value caching from scratch for autoregressive generation. Tests attention mechanics, tensor-shape reasoning, causal masking, position handling, and verification against full recomputation.

  Machine LearningTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Debug a broken Transformer implementation

  This question evaluates a candidate's ability to debug and validate a Transformer implementation, focusing on attention masking, parameter initialization, loss alignment, and other implementation-level correctness issues.

  Machine LearningOnsite