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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
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- 546
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- 11
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- 8
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Showing 151–180 of 782 results
- DandyMediumMachine Learning EngineerWhy do transformers struggle with long context?
This question evaluates understanding of transformer architectures, attention mechanisms, and scalability trade-offs, assessing competencies in computational complexity, memory usage, and system-level performance implications.
Machine LearningHR Screen - DatabricksMediumMachine Learning EngineerDelete a Character From Cover
This problem tests your ability to manage dynamic string indices by maintaining a set of active intervals and adjusting them efficiently after a character deletion.
Coding & AlgorithmsOnsiteStringsIntervalsArrays - DatabricksMediumMachine Learning EngineerDesign Harmful Content Detection
This problem tests the ability to design an end-to-end machine learning system for multimodal harmful content detection at scale, focusing on pipeline architecture, latency, and data governance.
ML System DesignOnsiteSystem DesignMultimodalScalability - DatabricksMediumMachine Learning EngineerDesign Harmful Content and OOM Detection
This question tests the ability to design an end-to-end ML system for real-time safety, covering taxonomy, data strategies, model architecture, and operational monitoring.
ML System DesignOnsiteSystem DesignMLOpsSafety - DatabricksMediumMachine Learning EngineerDesign In-Memory QPS Counter
This question evaluates the ability to design and implement an in-memory time-series counter and sliding-window aggregation, emphasizing efficient data structures, handling high-frequency events, and memory-time trade-offs.
Coding & AlgorithmsTechnical Screen - DatabricksMediumMachine Learning EngineerDetect Harmful Content in LLM Prompts and Responses
Design a machine learning system that detects harmful content in prompts sent to, and responses produced by, a large language model. Tests policy-driven framing, labeling and adversarial data, layered classifiers under latency limits, per-category evaluation, streaming checks and monitoring.
ML System DesignOnsite - DatabricksMediumMachine Learning EngineerImplement Gradient Descent Regression
This problem tests your understanding of linear regression by requiring the implementation of a gradient descent optimizer from scratch to minimize the Mean Squared Error loss.
Machine LearningOnsiteLinear RegressionGradient DescentOptimization - DatabricksMediumMachine Learning EngineerImplement Linear Regression Gradient Descent
This question tests the ability to implement batch gradient descent for simple linear regression from scratch, requiring the derivation of gradients for weights and bias, and understanding how to optimize convergence.
Machine LearningOnsiteGradient DescentLinear RegressionOptimization - DatabricksMediumMachine Learning EngineerImplement Simple Linear Regression with Gradient Descent from Scratch
Write simple linear regression trained with gradient descent from scratch, choosing the loss, learning rate and stopping rule yourself. Tests deriving the gradients correctly, writing a runnable training loop, understanding feature scaling and convergence, and verifying results against a closed-form fit.
Machine LearningOnsite - DatabricksMediumMachine Learning EngineerRetrieve and Rerank Tables and Notebooks for a Data-Aware Coding Agent
Design the retrieval system that lets an internal coding agent find the right tables and notebooks to answer data questions. Tests what to index for each asset type, hybrid retrieval plus reranking, handling noisy or stale assets, access control, and metric design from retrieval to final answers.
ML System DesignOnsite - DatadogHardMachine Learning EngineerImplement Focal Loss for Class-Imbalanced Classification
Tests the ability to implement binary focal loss with raw logits from first principles, emphasizing numerical stability, custom weighting, and tensor reduction strategies.
Machine LearningTechnical Screenmachine-learningdeep-learningpytorch - DatadogHardMachine Learning EngineerImplement Grouped-Query Attention (GQA)
Implement the Grouped-Query Attention (GQA) mechanism commonly used in modern LLMs to balance serving memory bandwidth with generation quality. You will handle tensor reshaping/expansion across query head groups, scaled dot-product attention, and causal masking.
Machine LearningTechnical Screendeep-learningtransformerspytorch - DaveEasyMachine Learning EngineerOwn and Resolve a Bug in Production
This question tests your ability to structure a high-stakes professional failure using the STAR method, focusing on technical accountability and systemic improvement.
Behavioral & LeadershipOnsiteBehavioralLeadershipProduction-Support - DaveEasyMachine Learning EngineerParse JSON Accounts into Typed Python Objects and Prepare Features
This problem tests your ability to sanitize and transform raw JSON account data into a structured format while enforcing strict numerical integrity and data consistency.
Data Manipulation (SQL/Python)OnsitePythonData CleaningJSON - DeshawMediumMachine Learning EngineerImplement sqrt and parity-based array rearrangement
In the Coding & Algorithms domain, this prompt evaluates numerical methods and precision handling for implementing a square-root function without library support, together with in-place array manipulation, partitioning and complexity reasoning for parity-based rearrangement, testing competencies in algorithm design, numerical approximation, and space-efficient data transformations. These problems are commonly asked to assess understanding of numerical stability, error bounds and iterative approximation as well as mastery of in-place ordering/partitioning strategies and time/space complexity analysis, requiring both conceptual understanding of mathematical properties and practical implementation ability.
Coding & AlgorithmsTechnical Screen - DisneyMediumMachine Learning EngineerBuild Naive Bayes spam classifier with F1
This question evaluates understanding of probabilistic text classification with Naive Bayes, text preprocessing and feature extraction, and use of the F1 score for performance measurement in a binary spam detection task.
Machine LearningTechnical Screen - DistylMediumMachine Learning EngineerDesign Streaming Speech and Multi-Model Chat Systems
A two-part system-design interview covering incremental multi-speaker transcription and a streaming multi-model chat service. Candidates must reason about ordering, revisions, backpressure, model fallback, durable conversation state, safety, and recovery.
ML System DesignOnsite - DoorDashMediumMachine Learning EngineerDesign a scalable recommendation serving system
This question evaluates an engineer's ability to design scalable, low-latency, and resilient online recommendation serving infrastructure, emphasizing caching strategies, partitioning/sharding, data storage consistency, and observability.
System DesignOnsite - DoorDashMediumMachine Learning EngineerDesign personalized restaurant search and recommendations
This question tests the ability to design an end-to-end personalized restaurant recommendation system, covering feature engineering, retrieval-ranking architecture, and infrastructure trade-offs.
System DesignTechnical ScreenRecommendation SystemsRankingSearch - DoorDashHardMachine Learning EngineerDiscuss ML infrastructure fundamentals
Discuss ML infrastructure fundamentals 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 DesignTechnical Screen - DoorDashMediumMachine Learning EngineerFind closest value to a target in a BST
This question evaluates understanding of binary search tree properties and numeric approximation, measuring competence in tree traversal, comparison logic, and algorithmic efficiency under input-size constraints.
Coding & AlgorithmsOnsite - DoorDashMediumMachine Learning EngineerHow would you prevent wrong items in deliveries?
This question evaluates systems thinking, root-cause analysis, metrics-driven decision-making, and cross-functional leadership in operational product-process contexts.
Behavioral & LeadershipOnsite - DoorDashMediumMachine Learning EngineerMinimize batches and allocate riders by time
This problem tests your ability to optimize resource scheduling by grouping tasks with overlapping time windows into the minimum number of batches, subject to capacity constraints.
Coding & AlgorithmsTechnical ScreenGreedyIntervalsBin Packing - DoorDashEasyMachine Learning EngineerWalk Through an ML Project
This question assesses the candidate's ability to communicate a full-lifecycle machine learning project, focusing on problem scoping, technical execution, and business impact.
Behavioral & LeadershipTechnical ScreenML LifecycleProduct ThinkingModel Deployment - ExaHardMachine Learning EngineerDebug a PyTorch Contrastive Loss
Repair a PyTorch contrastive loss whose matrix multiplication contracts incompatible dimensions instead of producing pairwise scores. Validate aligned rank-two tensors and temperature, create the correct batch-by-batch logits, preserve gradients, and handle low-precision numerical stability.
Machine LearningTechnical Screen - ExaHardMachine Learning EngineerDesign a Streaming Percentile Estimator
Design a streaming percentile estimator without storing every observation, and define rank error precisely. Compare a deterministic single-stream summary with t-digest-style centroids across memory, update cost, duplicates, skew, tail accuracy, and distributed merge limitations.
Statistics & MathTechnical Screen - FaireMediumMachine Learning EngineerData quality, lineage, monitoring and backfill design for an ML feature store
A discussion-based ML infrastructure design round on keeping the data behind models reliable and well governed. It covers defining and measuring availability, freshness, completeness, validity, consistency, distribution, compliance and lineage, monitoring a feature store, and planning a safe, correct data backfill.
ML System DesignOnsite - FaireMediumMachine Learning EngineerDesign a real-time model inference platform: deployment, scaling and monitoring
An ML system design question asking for a real-time model inference platform that teams use to serve trained models inside their applications' request paths. It tests safe model deployment and rollback, per-model autoscaling and batching, failure handling, and monitoring from infrastructure up to model quality.
ML System DesignOnsite - FigmaMediumMachine Learning EngineerClarify a BERT Keyphrase Span-Extraction Task
Implement and clarify the post-processing decoder for a BERT-based span-extraction model that maps token-level start and end logits back to non-overlapping character-level keyphrases.
Machine LearningTechnical Screennlpbertspan-extraction - FigmaMediumMachine Learning EngineerDesign Feed Ranking and HTML Generation
This problem tests the design of a dual-purpose ML system: a personalized content feed ranker and an LLM-based component that translates user intent into rendered HTML widgets.
ML System DesignOnsiteRankingLLMRecommendation Systems
Databricks
Datadog
Disney
DoorDash
Faire
Figma