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Machine Learning Engineer Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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- 546
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Showing 1–30 of 37 results
- AnthropicHardMachine Learning EngineerDebug a NumPy Extremely Randomized Trees Classifier
Anthropic Fellows debugging-OA practice: find the eight crash and accuracy bugs in a from-scratch NumPy Extra-Trees classifier until it matches scikit-learn.
Machine LearningOnline AssessmentAnthropic FellowsAI Safety Fellowshipdebugging - AnthropicHardMachine Learning EngineerExtend an LLM Inference Server with a Prefix KV Cache
Anthropic Fellows OA practice: add exact-match, prefix and shared-prefix KV caching with an LRU token budget to an existing LLM inference server, keeping greedy outputs identical.
ML System DesignOnline AssessmentAnthropic FellowsAI Safety Fellowshipllm-inference - AnthropicHardMachine Learning EngineerImplement a Simplified DNS Resolver
Implement an in-memory DNS resolver supporting A and CNAME record lookups, domain name normalization, TTL expiration, and CNAME alias chain cycle detection.
Coding & AlgorithmsOnline AssessmentHash TableGraphDesign - Capital OneHardMachine Learning EngineerCompute minutes since last train departure
This problem tests your ability to manipulate time objects, handle cyclic day-wrap scenarios, and perform efficient searching on sorted lists.
Coding & AlgorithmsOnline AssessmentArraysSortingTime Complexity - Capital OneMediumMachine Learning EngineerDescribe projects, conflicts, and tough stakeholders
This question evaluates project ownership, technical troubleshooting, cross-team communication, conflict resolution, and stakeholder management competencies for a Machine Learning Engineer within the Behavioral & Leadership domain.
Behavioral & LeadershipOnline Assessment - Capital OneMediumMachine Learning EngineerImplement string/matrix simulations and counting pairs
This problem tests string manipulation, simulation of grid-based gravitational collapse, and efficient combinatorial counting of string combinations.
Coding & AlgorithmsOnline AssessmentStringsSimulationHashing - Capital OneMediumMachine Learning EngineerRotate matrix and find max lamp coverage
This question evaluates array and matrix manipulation, in-place algorithm design, interval processing (sweep-line) and interval merging skills, testing competencies in algorithm design, complexity analysis, and robust edge-case reasoning within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - Capital OneMediumMachine Learning EngineerSimulate round-robin package assignment to servers
This problem tests array indexing manipulation and logic for stateful resource allocation, specifically handling circular queues with dynamic availability constraints.
Coding & AlgorithmsOnline AssessmentArraysSimulationQueueing - Capital OneMediumMachine Learning EngineerSolve Four Coding Assessment Tasks
This multi-part coding question evaluates array manipulation, algorithm simulation, deterministic spatial placement, and constrained optimization competencies across tasks involving cumulative thresholds, iterative subtraction sequences, fixed-shape grid placement, and minimal-increment adjustments for monotonic patterns.
Coding & AlgorithmsOnline Assessment - Capital OneEasyMachine Learning EngineerSolve OA tasks on string, grid path, subarrays
This problem tests proficiency in string manipulation, grid traversal with condition-based movement, and linear-time subarray analysis.
Coding & AlgorithmsOnline AssessmentArraysDynamic ProgrammingGrid Traversal - Capital OneHardMachine Learning EngineerSupport updates and count target-sum pairs
This problem tests the ability to maintain frequency counts of elements in a dynamic dataset and perform efficient range-sum queries for pairs that satisfy a target condition.
Coding & AlgorithmsOnline AssessmentHash MapData StructuresFrequency Array - CoinbaseHardMachine Learning EngineerExplain precision/recall and compute NN output
This question tests the ability to calculate classification performance metrics, explain ensemble model dynamics, and perform manual forward propagation through a small neural network architecture.
Machine LearningOnline AssessmentClassificationNeural NetworksEnsemble Methods - CoinbaseHardMachine Learning EngineerImplement local maxima, bagging, and k-means
This problem tests array traversal for local maxima detection, statistical resampling techniques for bagging, and iterative convergence for k-means clustering.
Coding & AlgorithmsOnline AssessmentAlgorithmsClusteringEnsemble Methods - DRWMediumMachine Learning EngineerBuild pipeline for imbalanced classification
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Build pipeline for imbalanced classification states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment - DRWMediumMachine Learning EngineerExplain core ML and DL fundamentals
Explain core ML and DL fundamentals 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 LearningOnline Assessment - DRWMediumMachine Learning EngineerProcess CSV for portfolio returns and metrics
This question evaluates a candidate's competency in time-series data manipulation, financial return computation, portfolio analytics, numerical stability, modular software design (clear function boundaries and unit tests), and algorithmic complexity analysis within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Online Assessment - DRWHardMachine Learning EngineerTrain LinearSVC to beat a hidden baseline
Train LinearSVC to beat a hidden baseline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment - Goldman SachsHardMachine Learning EngineerComplete decision tree and gradient descent functions
This question evaluates implementation skills in supervised machine learning and numerical optimization, specifically computing Gini impurity and selecting optimal binary splits for a decision tree alongside deriving and applying a single-step gradient descent update for linear regression.
Coding & AlgorithmsOnline Assessment - Goldman SachsHardMachine Learning EngineerEvaluate TPR/FPR, sigmoid, and activations
This question evaluates competency in binary classification metrics (confusion matrices, TPR/FPR), basic neural network forward computation (sigmoid neuron z and output), and knowledge of activation function selection and pitfalls.
Machine LearningOnline Assessment - HackerRankMediumMachine Learning EngineerBuild A Purchase-Intent Prediction Notebook
Prepare for a machine learning take-home notebook that predicts purchase intent from session features and submits binary outcomes. The prompt focuses on data cleaning, missing values, categorical encoding, model pipelines, weighted F1, threshold tuning, and robust submissions.
Machine LearningOnline Assessment - HackerRankMediumMachine Learning EngineerCount Good Subsequences With Equal Character Frequencies
Practice a combinatorics coding problem that counts good subsequences where every selected character has the same frequency. The prompt targets frequency counting, combinations, modular arithmetic, and grouping subsequences by common selected count.
Coding & AlgorithmsOnline Assessment - Jump TradingMediumMachine Learning EngineerSolve stock trading with pattern rules
This set of problems evaluates algorithmic skills in pattern recognition, state management, and sequence processing, specifically testing pattern-based decision rules on time-series stock data and iterative substring elimination in string processing within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - Jump TradingMediumMachine Learning EngineerSolve stock-pattern trading and substring elimination
This question evaluates algorithmic skills in array pattern recognition and string processing, specifically detecting day-to-day sign patterns for simulated trading and repeated elimination of fixed two-character substrings; it belongs to the Coding & Algorithms domain for a Machine Learning Engineer position and targets practical application of linear-time, constant-extra-space techniques. It is commonly asked to evaluate efficient O(n) algorithm design, handling of stateful streaming simulations and in-place string reduction under tight time/space constraints, and correctness reasoning for edge cases.
Coding & AlgorithmsOnline Assessment - MetaMediumMachine Learning EngineerImplement exponentiation and fill grid distances
This problem tests proficiency in efficient modular exponentiation (binary exponentiation) and multi-source Breadth-First Search (BFS) for computing shortest distances in a grid.
Coding & AlgorithmsOnline AssessmentBFSModular ArithmeticDivide and Conquer - MetaMediumMachine Learning EngineerRead Current and Historical Field Values with Expiration
Design an in-memory key-field-value store that supports historical queries, time-to-live (TTL) expiration, and field deletions without corrupting past states.
Coding & AlgorithmsOnline AssessmentBinary SearchDesignHash Table - MetaEasyMachine Learning EngineerSolve four OA string/array/matrix/graph tasks
This problem tests foundational proficiency in four distinct domains: string processing, array simulation, matrix transformation, and graph traversal.
Coding & AlgorithmsOnline AssessmentStringsSimulationBFS - NetflixEasyMachine Learning EngineerML 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 - NetflixEasyMachine Learning EngineerMultiply Two Sparse Integer Matrices Given as Dense Grids
Given two integer matrices stored as dense lists of rows in which most entries are zero, return their exact matrix product. It tests handling matrix dimensions correctly and exploiting sparsity to skip work on zero entries while still producing every entry of the result.
Coding & AlgorithmsOnline Assessment - PinterestHardMachine Learning EngineerAnswer core ML fundamentals questions
This question evaluates a candidate's mastery of core Machine Learning fundamentals, including classification metrics (precision and recall) with confusion-matrix interpretation, gradient concepts for optimization, ensemble learning tradeoffs, and the mechanics of a neural network forward pass.
Machine LearningOnline Assessment - PinterestHardMachine Learning EngineerImplement Naive Bayes classifier from scratch
This question evaluates understanding of probabilistic classification, specifically competence in estimating class priors, feature likelihood parameters, and computing posterior scores for Naive Bayes variants.
Machine LearningOnline Assessment
Anthropic
Capital One
Coinbase
DRW
Goldman Sachs
Meta
Netflix
Pinterest