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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 451–480 of 782 results
- OpenAIHardMachine Learning EngineerDesign LLM search handling long token inputs
Design an LLM search and RAG system that handles long documents and long user queries. Covers chunking, hierarchical retrieval, query compression, context budgeting, citations, latency, and evaluation.
ML System DesignOnsite - OpenAIMediumMachine Learning EngineerDesign Real-Time Collaborative Editing
This question tests your ability to design a low-latency, distributed synchronization system that manages concurrent text edits from multiple users while maintaining eventual consistency.
System DesignTechnical ScreenDistributed SystemsCRDTConcurrency - OpenAIMediumMachine Learning EngineerDesign a Real-Time Sensor Intelligence System
This question evaluates proficiency in end-to-end machine learning system design for real-time sensor-driven products, covering sensor choice trade-offs, data collection and labeling pipelines, model selection and real-time inference, and operational constraints such as latency, reliability, privacy, efficiency, and power.
ML System DesignOnsite - OpenAIMediumMachine Learning EngineerDesign a Resumable Iterator with Checkpoint and Restore
This question evaluates a candidate's ability to design serializable, stateful abstractions in both in-memory and I/O-bound contexts. It tests understanding of iterator patterns, checkpoint-restore semantics, and test-driven development — competencies commonly assessed in engineering roles requiring systems thinking and API design rigor.
Coding & AlgorithmsTechnical Screen - 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 low-latency RAG system
This question evaluates ML system design skills, specifically the ability to architect retrieval-augmented generation pipelines under strict latency, cost, and multi-tenancy constraints. It tests how candidates reason about vector search, re-ranking, and LLM integration at production scale — a core competency for senior machine learning engineering roles.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign a production RAG system
An OpenAI ML system design interview question (MLE technical screen): design a production, multi-tenant retrieval-augmented generation (RAG) system for enterprise document QA. The model answer covers ingestion and chunking, embedding selection, ANN/vector indexing (HNSW vs IVF-PQ), hybrid retrieval and cross-encoder re-ranking, prompt orchestration with citation grounding, hallucination mitigation, caching and freshness, multilingual and PII/safety handling, scalability and disaster recovery, observability and offline/online evaluation, an API and data schema, cost controls, and a phased rollout. It tests trade-off reasoning across recall, latency, cost, and compliance.
ML System DesignTechnical Screen - 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 - OpenAIMediumMachine Learning EngineerDesign an Editable Text Buffer
This problem tests your ability to design an efficient data structure for text manipulation while implementing undo/redo history and a dynamic prefix-based autocomplete feature.
Coding & AlgorithmsTechnical ScreenData StructuresTrieUndo-Redo - 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 In-Memory Key-Value Cache
Implement a thread-safe in-memory key-value cache supporting capacity bounds, LRU eviction, and per-key time-to-live (TTL) expiration.
Software Engineering FundamentalsTechnical ScreenData StructuresConcurrencyLRU Cache - OpenAIHardMachine Learning EngineerDesign an ML search system
This question evaluates system-design and information-retrieval competencies for machine-learning-powered enterprise document search, covering ingestion and indexing pipelines, hybrid lexical and semantic retrieval, feature generation and ranking, multi-tenant access control, and security/compliance.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign an ML search system with RAG
Design an ML search system with RAG 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 - OpenAIMediumMachine Learning EngineerDesign an OOD detection system
This question tests the ability to design an end-to-end system for detecting out-of-distribution (OOD) data in production LLMs to ensure reliability and safety.
ML System DesignTechnical ScreenLLMMLOpsOOD - 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 - OpenAIHardMachine Learning EngineerDesign enterprise RAG search system
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDiagnose Transformer training and inference bugs
This question evaluates practical debugging skills for Transformer-based sequence models, targeting machine learning engineers who must diagnose intermittent shape/dtype errors and convergence failures simultaneously. It tests deep familiarity with the full training stack — data pipelines, attention masking, positional encoding, optimizer precision, and distributed setup — and the ability to reason about how a single root cause can manifest across multiple symptoms.
Machine LearningTechnical Screen - 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
OpenAI