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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 211–240 of 351 results
- OpenAIHardMachine Learning EngineerDesign a chatbot fallback for unknown questions
This question evaluates a candidate's competency in ML system design, focusing on handling model uncertainty, retrieval-augmented components, tool integration, decision policies, and operational evaluation within the ML System Design domain.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a harmful video content moderation system
This question evaluates a candidate's competency in designing scalable, multi-modal machine learning systems for content moderation, covering architecture, model strategy, labeling and human-in-the-loop workflows, metrics, auditability, and defenses against adversarial evasion.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a recommendation system end-to-end
This question evaluates a candidate's competency in large-scale machine learning system design, including architecture for candidate generation and ranking, feature engineering across real-time and batch pipelines, training and serving workflows, and monitoring for fairness and safety.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a regional surge pricing strategy
This question evaluates skills in designing real-time surge pricing systems, assessing competencies in machine learning modeling, control and pricing logic, data engineering, operational robustness, explainability, and monitoring within the System Design domain.
System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a search query autocomplete system
This question evaluates a candidate's ability to design a real-time, low-latency search autocomplete system, exercising competencies in architecture, indexing and serving, ranking and personalization, typo-tolerance, multilingual support, abuse prevention and privacy-aware data handling; it is categorized under System Design and emphasizes practical application with architectural-level conceptual reasoning. It is commonly asked to assess how candidates balance stringent latency and scalability requirements with personalization and trending signals, robustness to input errors and multiple languages, and metrics-driven trade-offs across caching, ranking and monitoring.
System DesignOnsite - OpenAIHardMachine Learning EngineerDesign an AWS fine-tuning platform for LLMs
This question evaluates a candidate's competency in designing cloud-native ML platforms for fine-tuning large language models, covering distributed training, multi-tenant security and isolation, cost controls, reproducibility, observability, checkpointing, and deployment workflows.
ML System DesignOnsite - OpenAIMediumMachine Learning EngineerDesign an Agent Harness and Evaluation System
Design a safe harness and evaluation platform for a tool-using AI agent. Address isolated execution, repeatable task sets, trajectory tracing, deterministic and rubric-based graders, repeated trials, privacy, regression gates, and reward-hacking risks.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign an Extensible Simulation Engine
This problem tests object-oriented design patterns and modular architecture by requiring the implementation of a simulation engine that remains agnostic to the specific rules of a two-player turn-based game.
Software Engineering FundamentalsOnsiteSystem DesignObject-Oriented DesignDesign Patterns - OpenAIHardMachine Learning EngineerDesign an image/video near-duplicate detection system
This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign and optimize a RAG system
An OpenAI machine learning engineer onsite ML system design question: design, optimize and evaluate a production Retrieval-Augmented Generation (RAG) system that answers questions over a private corpus with citations. The merged answer covers the full pipeline — ingestion and layout-aware parsing, structure-aware chunking, embeddings and versioning, hybrid BM25 plus vector retrieval with RRF fusion, cross-encoder reranking, grounded generation with forced citations, abstention and a fallback ladder, ACL enforcement during retrieval, and the insert/edit/delete freshness lifecycle. It then gives a stage-by-stage evaluation plan (chunk answerability, hit-rate@K, recall@K, nDCG, faithfulness, citation quality, end-to-end success), how to bootstrap labels with no gold set, and follow-ups on debugging a wrongly cited document, embedding-model upgrades, multi-hop versus list-all queries, near-duplicates, conflicting sources and validating an LLM judge.
ML System DesignOnsite - OpenAIMediumMachine Learning EngineerExplain KV cache in Transformer inference
This question evaluates understanding of KV cache mechanisms in Transformer inference, including attention-state caching, memory and latency trade-offs, and engineering optimizations for autoregressive decoding.
Software Engineering FundamentalsOnsite - OpenAIHardMachine Learning EngineerExplain motivation and mission alignment
This question evaluates motivation, mission alignment, cultural fit, and communication skills by probing personal values, long-term goals, and the ability to articulate congruence with an organization's purpose.
Behavioral & LeadershipOnsite - OpenAIHardMachine Learning EngineerExplain what torch.distributed.barrier does
This question evaluates understanding of PyTorch distributed synchronization primitives, process coordination, and concurrency control in distributed training workflows.
Software Engineering FundamentalsOnsite - OpenAIMediumMachine Learning EngineerFilter Bad Human Annotations
Tests the ability to detect and filter low-quality human labels in large-scale dataset pipelines by modeling annotator reliability, distinguishing genuine ambiguity from noise, and evaluating downstream model performance.
Machine LearningOnsitedata-centric-airlhflatent-variable-models - OpenAIMediumMachine Learning EngineerFind Minimum Compatible Version
Implement an algorithm to find the earliest compatible software version while minimizing expensive compatibility-check API calls across partitioned version branches.
Coding & AlgorithmsOnsiteBinary SearchAlgorithmsOptimization - OpenAIMediumMachine Learning EngineerFind the First Working Version
This question tests the ability to combine custom comparison logic with a binary search approach to identify the first transition point in a monotonic sequence.
Coding & AlgorithmsOnsiteBinary SearchAlgorithmsOptimization - OpenAIHardMachine Learning EngineerHand-Rolled Backprop: Running Product and Stable Cross-Entropy
Derive and implement, without an autodiff library, the backward pass of a running (cumulative) product in linear time without division, then a numerically stable softmax cross-entropy and its gradient. Tests chain-rule fluency, prefix/suffix tricks, and gradient checking.
Machine LearningOnsiteautodiffbackpropagationnumerical-stability - OpenAIHardMachine Learning EngineerHand-Written Gradients: Prefix-Product Backward Pass and Stable Cross-Entropy
Write gradients by hand: the forward and backward pass of the prefix-product operation, in linear time and without division so inputs may contain zeros, and a numerically stable softmax cross-entropy loss with its gradient in NumPy. It tests the chain rule, prefix and suffix products, and gradient checking.
Machine LearningOnsite - OpenAIMediumMachine Learning EngineerImplement Prefix Products and Their Backward Pass
Work through inclusive prefix products in separate-output, in-place, reverse-gradient, and parallel variants. Explain zeros, overflow, mutation and aliasing, constant temporary workspace, synchronization, floating-point effects, and the work/depth trade-offs of each part.
Machine LearningOnsite - OpenAIHardMachine Learning EngineerImplement in-memory database insert and delete operations
This question evaluates understanding of data structures and algorithmic complexity in the context of designing a key-value in-memory database, and it falls under the Coding & Algorithms domain for a Machine Learning Engineer role while focusing on practical implementation skills.
Coding & AlgorithmsOnsite - OpenAIMediumMachine Learning EngineerImplement vectorized NumPy ops and explain broadcasting
This question evaluates proficiency in vectorized numerical computing with NumPy, understanding of broadcasting semantics, and awareness of numerical stability and algorithmic time/space complexity when manipulating high-dimensional arrays.
Data Manipulation (SQL/Python)Onsite - OpenAIHardMachine Learning EngineerImprove Training With Noisy Annotators
This question evaluates a candidate's ability to handle label noise by estimating annotator reliability and applying data-cleaning, relabeling, or reweighting methods while measuring model performance and understanding classification metrics.
Machine LearningOnsite - OpenAIMediumMachine Learning EngineerInfer Generic Return Types
Implement a toy type-inference engine that unifies generic parameter types with concrete argument types and substitutes resolved type variables to determine the return type.
Coding & AlgorithmsOnsiterecursiontreetype-systems - OpenAIMediumMachine Learning EngineerMaintain Entropy for a Streaming Distribution
Maintain empirical Shannon entropy after every categorical observation without rescanning all categories. The discussion should cover arbitrary identifiers, first-item behavior, exact counts, floating-point drift, concurrency, deletions, sliding windows, and merging summaries from separate stream partitions.
Statistics & MathOnsite - OpenAIHardMachine Learning EngineerRepair a Broken Tiny Language Model, Then Make Decoding Incremental
Find four planted bugs in a compact PyTorch decoder-only language model (some produce NaNs, some silently ruin learning), then add key/value caching so generation processes one new token per step.
Machine LearningOnsitepytorchtransformersdebugging - OpenAIHardMachine Learning EngineerSchedule Incremental Labeling Tasks
This problem tests the ability to design an incremental task scheduler that balances workload across a pool of annotators while maintaining fairness and deterministic state transitions.
Coding & AlgorithmsOnsiteHeapsSchedulingGreedy - OpenAIHardMachine Learning EngineerSearch Monster Battle Strategies
Design a search algorithm using state-space exploration to find the minimum number of turns required to defeat a boss given transient health, scripted boss attacks, and a single-use revive.
Coding & AlgorithmsOnsiteBreadth-First SearchState Space SearchGraph Algorithms - OpenAIHardMachine Learning EngineerSelect high-quality math documents from crawls
This question assesses the ability to design a scalable pipeline that ingests, cleans, extracts, and filters high-quality mathematical content from diverse, noisy web-crawled sources.
ML System DesignOnsiteSystem DesignNLPData Engineering - OpenAIHardMachine Learning EngineerSimulate Plant Infection With Controlled Burning
This question evaluates a candidate's ability to implement discrete-time grid simulations with delayed state transitions, reason about simultaneous updates and neighborhood effects, and perform a limited combinatorial optimization (choosing one row or column) to minimize a global outcome.
Coding & AlgorithmsOnsite - OpenAIMediumMachine Learning EngineerSimulate Turn-Based Monster Battles
This problem tests your ability to design a stateful simulation engine that handles turn-based mechanics, complex status modifiers, and dynamic combat interactions.
Coding & AlgorithmsOnsiteSimulationState ManagementGame Logic
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