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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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- 11
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Showing 751–780 of 782 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 - WaymoMediumMachine Learning EngineerDesign Large-Scale Inference Serving
This question evaluates understanding of large-scale ML inference systems, assessing competencies in capacity planning, latency and tail-latency engineering, memory and bandwidth estimation, hardware selection (CPUs/GPUs/specialized accelerators), batching and caching trade-offs, and reliability concerns such as out-of-memory prevention and recovery. It is commonly asked to test practical system-design skills for production deployment by requiring back-of-the-envelope QPS and resource estimates and reasoning about operational trade-offs; this belongs to the ML system design category and emphasizes practical application over purely conceptual theory.
ML System DesignTechnical 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 EngineerOptimize Tensor Runtime Kernels
This question evaluates competency in optimizing machine learning framework runtimes for accelerators, covering profiling and performance analysis of tensor kernels, memory layout design, operator scheduling and kernel fusion while weighing trade-offs among throughput, latency, memory usage, numerical correctness, and maintainability.
Software Engineering FundamentalsTechnical Screen - 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 - WhatnotMediumMachine Learning EngineerDesign trending livestream discovery
This question evaluates expertise in designing end-to-end ML-driven recommendation systems, including competencies in real-time feature engineering, candidate generation, model serving, business-rule integration, monitoring, experimentation, and operational trade-offs for live content.
ML System DesignTechnical Screen - WhatnotMediumMachine Learning EngineerImprove and debug the shopping app
This question evaluates product-minded engineering competencies including product intuition, user-experience assessment, telemetry-driven diagnostics, funnel and segmentation analysis, and debugging/observability skills relevant to a machine learning engineer.
System DesignTechnical Screen - XPengHardMachine Learning EngineerDesign and train a PPO pipeline
This question evaluates proficiency in designing and training Proximal Policy Optimization (PPO) pipelines, covering environment interfacing, observation/action design, reward shaping, rollout and advantage estimation, hyperparameterization, normalization, parallelization, evaluation, and sim‑to‑real considerations.
ML System DesignTechnical Screen - XPengMediumMachine Learning EngineerDiscuss resume and company fit
This question evaluates a candidate's competence in reinforcement learning and production-grade ML systems, focusing on technical decision-making, measurable project impact, communication of role and motivation, and leadership in planning within the Behavioral & Leadership domain.
Behavioral & LeadershipTechnical Screen - XPengHardMachine Learning EngineerExplain PPO and Transformer basics
This question evaluates mastery of reinforcement learning algorithms (PPO, policy gradients, TRPO), value-function theory and actor–critic methods (Bellman equations), on- versus off-policy trade-offs and training-stability techniques (clipping, entropy, advantage estimation), plus sequence-modeling fundamentals with Transformers (self-attention, positional encodings and complexity comparisons). It is commonly asked to judge reasoning about algorithmic trade-offs, sample efficiency and stability in practical ML systems, and falls under the Machine Learning domain—specifically reinforcement learning and deep learning/sequence modeling—testing both conceptual understanding and practical application.
Machine LearningTechnical Screen - XometryMediumMachine Learning EngineerExplain an End-to-End ML Project
This question evaluates leadership and end-to-end machine learning skills, including problem framing, metric definition, data handling, model selection, training and validation practices, deployment engineering, and measurement of business impact.
Machine LearningTechnical Screen - XometryMediumMachine Learning EngineerOptimize Model Serving Under 200ms
This question evaluates competency in deploying and optimizing machine learning models for low-latency online inference, covering model serving, latency profiling, hardware considerations, and managing accuracy–latency trade-offs within a 200ms SLO in the ML System Design domain.
ML System DesignTechnical Screen - YahooEasyMachine Learning EngineerML Concepts: Overfitting, Bias-Variance Trade-off, Class Imbalance, Missing Data
A machine learning fundamentals round covering overfitting and underfitting, the bias-variance trade-off, class imbalance, and missing data. It tests whether a candidate can define each concept precisely, diagnose it from training and validation evidence, and choose remedies while weighing their side effects.
Machine LearningTechnical Screen - 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 EngineerExplain why LLMs produce hallucinations
This question evaluates a candidate's understanding of the causes and mitigation of large language model hallucinations, covering competencies in probabilistic training objectives, data characteristics, model and optimization limitations, inference-time behavior, and awareness of mitigation strategies.
Machine LearningTechnical 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 EngineerDebug Feature Updates in a Laptop Management CLI
Implement and debug a stateful command-line interface processor that tracks device feature configurations, updates targeted laptops without state leakage across devices, and rolls back on validation failures.
Software Engineering FundamentalsTechnical ScreenState ManagementCLIData Structures - 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 EngineerDescribe your research and contributions
Describe your research and contributions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - 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 EngineerDesign a house-price prediction workflow
Design a house-price prediction workflow 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 - xAIMediumMachine Learning EngineerDynamic Batching Decode Loop for a Mock Language Model with Slot Refilling
An LLM inference coding question: write the generation loop for a mock language model that returns one next token per sequence in a batch. Requests finish at different steps on a stop token or a max-token limit, and freed batch slots must be refilled from a waiting queue, testing state tracking and batching logic.
Software Engineering FundamentalsOnsite - xAIMediumMachine Learning EngineerExplain Data Collection Choices and Their Impact on Downstream Model Performance
A machine learning screening question: explain how the data for a project on your resume was collected and processed, then discuss how data choices affect performance on downstream tasks. It tests first-hand knowledge of data pipelines, causal reasoning about data quality, and how to measure data effects.
Machine LearningOnsite - xAIMediumMachine Learning EngineerFind All Faulty GPU Nodes with Parallel Group Tests That Cannot Share Nodes
An algorithm design question about finding every faulty GPU node when the only tool is a group test that fails if any node in the set is bad. Tests need at least two nodes and parallel tests cannot share nodes, so it tests adaptive testing strategies, identifiability, and cost in rounds and tests.
Software Engineering FundamentalsOnsite - 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 - xAIHardMachine Learning EngineerImplement a trie-based tokenizer
This question evaluates a candidate's competency in designing and implementing a production-grade subword tokenizer, covering trie-based longest-prefix matching, Unicode-aware text processing, normalization and casing impacts, whitespace/punctuation rules, fallback strategies, performance and memory trade-offs, versioning, and testing for edge cases. It is commonly asked in ML System Design interviews to assess practical implementation skills and conceptual trade-off reasoning for deterministic, scalable LLM preprocessing, and tests both practical application and conceptual understanding within the ML System Design domain.
ML System DesignTechnical Screen
Waymo
Whatnot
XPeng
Yahoo
Zoox
xAI