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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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Showing 121–150 of 351 results
- LinkedInMediumMachine Learning EngineerAnswer practical ML foundations questions
This question evaluates an interviewee's practical mastery of ML foundations, covering probability calibration and its evaluation, feature selection methods including neural-network approaches, tokenizer types and trade-offs in NLP, and optimizer behavior such as Adam's maintained statistics.
Machine LearningOnsite - LinkedInMediumMachine Learning EngineerDesign LinkedIn Learning course recommendations
The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.
ML System DesignOnsite - LinkedInMediumMachine Learning EngineerDesign a Skills inference system
This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.
ML System DesignOnsiteNLPEntity LinkingRecommendation Systems - LinkedInMediumMachine Learning EngineerDesign a system for LinkedIn Skills
This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.
ML System DesignOnsite - LinkedInMediumMachine Learning EngineerGenerate uniform 0–6 from biased coin
This question evaluates understanding of randomized algorithms, probability theory, and techniques for extracting uniform randomness from a biased source.
Coding & AlgorithmsOnsite - LinkedInMediumMachine Learning EngineerImplement alert queries and spike detection
This problem tests your ability to process a stream of events with time-based constraints, requiring an efficient sliding-window approach and the use of monotonic data structures for spike detection.
Coding & AlgorithmsOnsiteSliding WindowData StructuresStreaming - LinkedInMediumMachine Learning EngineerPlan and lead a large recommendation project
This Behavioral & Leadership question evaluates leadership, execution planning, cross-functional coordination, resourcing, and stakeholder communication skills in the context of machine learning recommendation product development.
Behavioral & LeadershipOnsite - LinkedInMediumMachine Learning EngineerSample index from probability distribution
This question tests the ability to implement a weighted random sampling algorithm efficiently using prefix sums and binary search to achieve sub-linear query time.
Coding & AlgorithmsOnsiteAlgorithmsProbabilityBinary Search - LinkedInMediumMachine Learning EngineerSample uniformly from a circle’s area
This question evaluates understanding of continuous probability distributions and geometric random sampling, testing whether a candidate recognizes how area-weighted uniformity differs from naive radial choices.
Coding & AlgorithmsOnsite - MercorMediumMachine Learning EngineerBuild a Candidate Search System
Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.
ML System DesignOnsiteml system designinformation retrievalnlp - MercorMediumMachine Learning EngineerCheck Whether Every Parcel Finds a Locker With Forward Overflow
Parcels arrive one at a time for a bank of numbered lockers, each requesting a preferred locker and overflowing to the next higher-numbered empty one if it's taken. Decide whether every parcel finds a locker. Tests precise rule-following and an efficient alternative to simulating forward scans one slot at a time.
Coding & AlgorithmsOnsiteunion-findsimulationarrays - MercorMediumMachine Learning EngineerDesign Candidate Search And LLM Evaluation Tasks
Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.
ML System DesignOnsite - MercorMediumMachine Learning EngineerDesign Work Orchestration for Machine-Learning Data Pipelines
Design a resilient, multi-tenant orchestration engine tailored for machine learning pipelines that manages versioned DAGs, heterogeneous compute routing, data quality gates, and atomic dataset publication.
ML System DesignOnsiteML InfrastructureSystem DesignWorkflow Orchestration - MercorMediumMachine Learning EngineerDesign a DAG Pipeline Runner for Data-Labeling Jobs
Design a multi-tenant service that runs user-defined DAGs of automated steps (pre-processing, model pre-labeling, QA checks, exports), tracks every run, and survives crashes. Tests data modeling, separating the scheduler from workers, durable state, retries and idempotency, tenant fairness and observability.
System DesignOnsiteworkflow-orchestrationdistributed-systemsstate-machines - MercorMediumMachine Learning EngineerDesign a High-Demand Event Ticket Marketplace
Design an event ticketing platform capable of handling flash-sale spikes without overselling, featuring transactional seat holds, idempotent payment processing, and virtual queueing.
System DesignOnsitesystem-designdistributed-systemsconcurrency - MercorMediumMachine Learning EngineerDesign a workflow orchestration system that runs multi-step jobs reliably
A system design question asking you to build a workflow system where users define multi-step workflows with dependencies, start runs, and track progress. It tests data modeling, orchestration and worker architecture, durable state, retries and idempotency, fairness between tenants, scaling and observability.
System DesignOnsite - MercorMediumMachine Learning EngineerImplement a Referral Network
Design and implement an in-memory referral network as a directed acyclic graph (DAG) supporting cycle-safe edge insertion, depth-bounded reachability queries, and expected reward computation under geometric decay.
Coding & AlgorithmsOnsiteGraph AlgorithmsDAGBFS - MercorMediumMachine Learning EngineerReview a Compact GPT-Style Transformer Implementation
Assess and debug a compact decoder-only Transformer implementation. Candidates trace tensor dimensions through causal multi-head self-attention, identify flaws in next-token loss alignment and KV-cached generation, and establish equivalence between cached and uncached forward passes.
Machine LearningOnsiteTransformersPyTorchDeep Learning - MetaMediumMachine Learning EngineerAnswer behavioral questions on projects and feedback
This set of behavioral questions evaluates communication, leadership, adaptability, handling ambiguity, feedback exchange, and project ownership within the context of machine learning engineering projects.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerAnswer core behavioral questions using STAR
This question evaluates behavioral and leadership competencies—including communication, teamwork, conflict resolution, time and scope management, and mentorship—within the context of a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
ML System DesignOnsite - MetaHardMachine Learning EngineerBuild a Friend Recommender
This problem tests your ability to implement a graph-based recommendation algorithm using common neighbors and popularity constraints to rank potential connections.
Coding & AlgorithmsOnsiteGraphSortingRecommendation System - MetaMediumMachine Learning EngineerBuild harmful-content text classifier
This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.
Machine LearningOnsiteNLPClassificationMLOps - MetaMediumMachine Learning EngineerCompute the next lexicographic permutation
This problem tests your ability to manipulate array elements in-place to find the next lexicographically ordered sequence, focusing on understanding the properties of permutations.
Coding & AlgorithmsOnsiteArraysPermutationsTwo Pointers - MetaMediumMachine Learning EngineerDebug and optimize a card-drawing strategy
This problem tests your ability to simulate card-drawing probabilities, debug flawed logic in iterative selection strategies, and optimize performance for repeated simulations.
Coding & AlgorithmsOnsiteAlgorithmsSimulationProbability - MetaHardMachine Learning EngineerDemonstrate leadership and ensure data compliance
This question evaluates leadership, program execution, stakeholder management, risk and tradeoff reasoning in machine learning engineering, along with expertise in data privacy, regulatory compliance, and operational data governance for user-data-driven systems.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerDescribe handling intense time pressure
This question evaluates a candidate's ability to perform under intense time pressure and multitask, specifically assessing prioritization, trade-off communication, stakeholder management, stress resilience, and execution skills relevant to machine learning engineering within a Behavioral & Leadership context.
Behavioral & LeadershipOnsite - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Large-Scale Personalized Recommendation System End to End
An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.
ML System DesignOnsite
LinkedIn
Mercor
Meta