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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.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 241–253 of 253 results
- WaymoMediumMachine Learning EngineerDebug Tensor Conversion Bugs
This question evaluates debugging and implementation-level skills in tensor and array manipulation, focusing on aliasing behavior, conversion between column lists and ndarrays, correct handling of non-divisible chunking, and writing tests for these issues.
Coding & AlgorithmsTechnical Screen - WaymoHardMachine Learning EngineerGenerate Instructions for a Blind Maze Robot
This question evaluates a candidate's ability to design algorithmic strategies for state-space synchronization, graph traversal, and reasoning under partial observability in grid environments.
Coding & AlgorithmsOnsite - WaymoMediumMachine Learning EngineerReturn All Words Matching Each Prefix From a Word List
Build a structure over a word list that answers prefix queries, returning every distinct word that starts with each prefix in lexicographic order. Tests prefix-tree design, deduplication, and answering many queries without rescanning the whole list.
Coding & AlgorithmsOnsite - YahooEasyMachine Learning EngineerRank Items by Dot-Product Similarity to a User Embedding, Excluding Seen Items
Rank candidate items for a user by the dot product of the user's embedding with each item's embedding, returning item indices from highest to lowest score with ties broken by index. A follow-up removes items the user has already seen, testing vectorized scoring, sorting and filtering.
Coding & AlgorithmsTechnical Screen - ZillowMediumMachine Learning EngineerImplement k-th largest in a number stream
This question evaluates proficiency with data structures and order-statistics in a streaming context, focusing on API design and time/space complexity trade-offs within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - ZillowMediumMachine Learning EngineerImplement placeholder-based pattern matcher
Implement placeholder-based pattern matcher evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - ZiprecruiterEasyMachine Learning EngineerFind Neighboring Records by Identifier
This question tests the ability to manage ordered sequences and perform O(log n) lookups to retrieve adjacent elements in a dataset.
Coding & AlgorithmsTechnical ScreenBinary SearchArraysEfficiency - ZooxMediumMachine Learning EngineerCompute Roller Coaster Scores
Tests string parsing, floating-point evaluation, conditional logic, and the implementation of an extensible rule-based scoring engine for ride telemetry.
Coding & AlgorithmsTechnical ScreenString ParsingImplementationDesign Patterns - xAIHardMachine Learning EngineerConstant-Time Insert, Remove, and Uniform Random Sampling
This question evaluates the ability to design a data structure that supports constant-time insertions, removals, and uniform random sampling, testing concepts such as hashing, indexed storage, and randomized selection within the coding and algorithms domain.
Coding & AlgorithmsOnsite - xAIMediumMachine Learning EngineerDesign O(1) random-sampling set
This question evaluates a candidate's competency in data-structure design, randomized algorithms, and algorithmic complexity analysis within the Coding & Algorithms domain, with a focus on achieving expected O(1) operations for insert, remove, and uniform sampling.
Coding & AlgorithmsTechnical Screen - xAIHardMachine Learning EngineerGreedy Longest-Match Tokenizer for an LLM Data Pipeline
This question evaluates understanding of string-processing and greedy maximal-munch tokenization, as well as competency in designing efficient prefix-matching data structures and handling large-scale text preprocessing for LLM pipelines.
Coding & AlgorithmsOnsite - xAIHardMachine Learning EngineerImplement Distributed Matrix Multiplication
This problem tests your ability to design a distributed matrix multiplication algorithm by partitioning large matrices across multiple worker nodes to minimize communication overhead.
Coding & AlgorithmsTechnical ScreenDistributed SystemsLinear AlgebraParallel Computing - xAIMediumMachine Learning EngineerImplement dynamic batching for token decoding
This question evaluates dynamic batching, per-request state management, and sequence-decoding correctness for language-model inference, including handling stop conditions, max-token limits, and maintaining a correct slot-to-request mapping.
Coding & AlgorithmsOnsite
Waymo
Yahoo
Zoox
xAI