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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
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- 11
- Topics
- 8
- Languages
Showing 691–720 of 782 results
- TikTokMediumMachine Learning EngineerExplain your VLM project end-to-end
This question evaluates proficiency in vision-language model engineering, including model architecture (vision encoder, language model, fusion), data curation and distribution, training recipes, evaluation metrics, inference latency, and limitations analysis within the Machine Learning domain of multimodal/Vision-Language models.
Machine LearningTechnical Screen - TikTokMediumMachine Learning EngineerFind and count target-sum subarrays
Find and count target-sum subarrays 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 - TikTokMediumMachine Learning EngineerFind length of longest common subsequence
This problem tests your ability to identify overlapping subproblems and optimal substructure by implementing a dynamic programming solution to find the length of the Longest Common Subsequence (LCS) between two strings.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingStringsOptimization - TikTokMediumMachine Learning EngineerFind longest substring without repeating characters
This question evaluates proficiency in string processing, duplicate-detection using appropriate data structures, and algorithmic complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - TikTokMediumMachine Learning EngineerFix a BST with two misplaced keys
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Fix a BST with two misplaced keys states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - TikTokHardMachine Learning EngineerGenerate all safe queen placements on board
This question evaluates algorithm design for constraint-satisfaction problems, testing skills in search, recursion, backtracking, combinatorics, and state-space pruning.
Coding & AlgorithmsTechnical Screen - TikTokHardMachine Learning EngineerImplement local maxima, bagging, and k-means
This problem tests your ability to implement a sliding window peak detector, a bootstrapping ensemble sampler, and a centroid-based clustering algorithm from scratch.
Coding & AlgorithmsOnline AssessmentAlgorithmsClusteringEnsemble Learning - TikTokHardMachine Learning EngineerWalk through resume under pressure and critique
A TikTok machine-learning-engineer onsite behavioral & leadership question: walk through four resume projects — problem and constraints, your ownership, architecture and key decisions, alternatives and trade-offs, measurable outcomes, and the hardest challenge — then defend your trade-offs under pushback, respond to blunt feedback, and adapt your communication to a different language or style. It evaluates technical ownership, systems thinking, experimental rigor, and composed, data-first communication under pressure.
Behavioral & LeadershipOnsite - TowerMediumMachine Learning EngineerExplain the KV Cache and Prefill-Decode Disaggregation in LLM Serving
Explain how the KV cache speeds up autoregressive decoding in transformer language models, estimate how much GPU memory it needs, and describe prefill-decode disaggregation in LLM serving. It tests understanding of compute-bound versus memory-bound inference phases, cache sizing and the trade-offs of splitting the phases across GPU pools.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerExplain ML basics and recommender tuning
This question evaluates core machine learning competencies including overfitting and regularization, ensemble methods like bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), hyperparameter tuning in production, and end-to-end recommender system design and deployment.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerImplement K-Means and compare with GMM
This question evaluates understanding and practical implementation of clustering algorithms, specifically K-Means centroid initialization, assignment and update mechanics, convergence criteria and edge-case handling, along with comparative knowledge of Gaussian Mixture Models and the Expectation-Maximization training framework.
Coding & AlgorithmsTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)
This question tests ML system design competency, specifically the ability to architect end-to-end recommendation pipelines covering retrieval, ranking, feature engineering, and low-latency serving at scale. It assesses practical knowledge of two-stage recommender systems, implicit feedback modeling, cold-start handling, and critical evaluation of AI-generated code — skills central to machine learning engineering interviews.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Thumbnail Selection for a Streaming Catalog
This ML system design question tests a candidate's ability to architect an end-to-end personalization and ranking system, covering problem framing, reward modeling, and low-latency serving at scale. It evaluates practical knowledge of contextual bandits, engagement-signal design, cold-start handling, and off-policy evaluation — core competencies for machine learning engineering roles.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerMachine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings
This question assesses foundational machine learning knowledge across multiple core domains, including tree-based models, supervised training, model evaluation, embeddings, and transformer architecture. It is commonly used in ML engineer interviews to gauge whether a candidate can explain key concepts with correct mechanics, articulate trade-offs, and reason through failure modes.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerWeighted Random Sampling by Index
This question tests a candidate's ability to implement weighted random sampling using prefix sums and binary search, evaluating practical algorithm design for probability distributions. It assesses proficiency in translating mathematical probability constraints into efficient data structures, a skill commonly probed in machine learning engineering roles where non-uniform sampling is fundamental to model training and inference pipelines.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumMachine Learning EngineerSolve tree partition and IPO allocation
This question evaluates algorithmic problem-solving skills in tree processing and allocation simulations, specifically assessing understanding of subtree-sum computation for tree partitioning and deterministic round-robin allocation logic for IPO-style share distribution, along with appropriate data structure selection and correctness under edge cases. It is commonly asked in the Coding & Algorithms domain because it measures practical application-level implementation and the ability to analyze time and space complexity rather than purely conceptual proof.
Coding & AlgorithmsOnline Assessment - UberEasyMachine Learning EngineerAnalyze sales with groupby
This question evaluates proficiency in data manipulation and analytics using pandas, specifically the ability to join relational tables and apply groupby aggregations to compute business metrics like total sales, average sales per order, and completion rates.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerAnswer leadership questions on tradeoffs and collaboration
This set of behavioral leadership questions evaluates leadership competencies including trade-off decision-making, mentorship, cross-functional collaboration, roadmap prioritization, and conflict resolution for a Machine Learning Engineer within the Behavioral & Leadership domain, focusing on practical application through recounting real project experiences. These prompts are commonly asked to probe both conceptual understanding of trade-offs and organizational dynamics and the practical application of influence, communication, and judgment when aligning stakeholders and prioritizing work.
Behavioral & LeadershipOnsite - UberMediumMachine Learning EngineerChoose K pickup locations minimizing L1 distance
This question evaluates facility-location and spatial optimization skills under the Manhattan (L1) metric, testing algorithm design, combinatorial optimization, and handling geometric data.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerCompute currency conversion via graph search
This question evaluates graph modeling and pathfinding with weighted edges, numerical reasoning about multiplicative rates and floating-point precision, and the ability to analyze algorithmic time and space complexity.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerCompute minimal time to finish dependent tasks
This question evaluates understanding of graph algorithms and scheduling, specifically reasoning about task dependencies, parallel execution, and cycle detection in directed graphs.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerCount connected delivery zones
This question evaluates graph traversal and connected-components detection on grids, measuring competence in grid-based algorithms, neighbor connectivity, and algorithmic analysis of time and space complexity.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerDescribe an ML system you built
This question evaluates end-to-end machine learning system design and ownership — covering problem framing, data and feature engineering, model choice, training and evaluation, deployment, monitoring, and measured impact — and it probes interpersonal leadership in handling conflicts over model decisions, metrics, or product trade-offs.
Behavioral & LeadershipTechnical Screen - UberMediumMachine Learning EngineerDesign a feed ranking system
This question evaluates a candidate's competency in designing end-to-end machine-learning-based feed ranking systems, including setting ranking objectives, candidate generation and filtering, feature design, model training and serving, and operational concerns like freshness, diversity, and scalability.
ML System DesignTechnical Screen - UberMediumMachine Learning EngineerDesign a restaurant recommendation system
An Uber MLE onsite ML system design question: design a personalized restaurant recommendation system for a food delivery marketplace's home feed. It tests candidate generation and ranking, feature engineering and training data, cold-start and exploration, low-latency serving, and offline/online evaluation, plus marketplace concerns like availability, ETA, diversity, fairness, and business rules.
ML System DesignOnsite - UberHardMachine Learning EngineerDesign real-time grid ETA for drivers
This question evaluates a candidate's competency in real-time ML system design, covering streaming data ingestion, GPS noise handling and denoising, online feature computation, low-latency model serving, and operational monitoring.
ML System DesignOnsite - UberEasyMachine Learning EngineerExplain and test completion-rate gaps
This question evaluates understanding of causal inference, product-metrics analysis, experimental design, and statistical hypothesis testing (including two-sample t-tests) as applied to operational problems in a marketplace.
Machine LearningTechnical Screen - UberHardMachine Learning EngineerFind Robots by Nearest-Blocker Distances
Implement an algorithm to identify all robots in a grid whose four-directional distances to the nearest obstacle or grid boundary match a target query.
Coding & AlgorithmsTechnical Screenmatrixprefix-sumhash-table - UberMediumMachine Learning EngineerFind a Black-Box Convex Function Minimum
This question evaluates proficiency in zero-order optimization and algorithm design, focusing on reasoning about convexity and unimodality when only function evaluations are available.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerFind robots in a grid whose four-direction distances to blockers match a query
Given a grid of robots, blockers and empty cells plus a query of four distances, return every robot whose distances to the nearest blocker or grid edge going up, down, left and right match the query exactly. Tests precise distance definitions, boundary handling and scaling past a per-robot scan.
Coding & AlgorithmsTechnical Screen
TikTok
Tubi
Two Sigma
Uber