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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 1–30 of 107 results
- AdobeHardMachine Learning EngineerDesign a natural-language AEP Q&A assistant
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 a natural-language AEP Q&A assistant states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AdobeHardMachine Learning EngineerOptimize LLM Training and Serving
This question tests the ability to diagnose hardware utilization bottlenecks in LLM workloads and design architectural optimizations to improve throughput and latency under constrained GPU memory resources.
ML System DesignOnsiteLLMGPU-ArchitecturePerformance-Engineering - AirbnbMediumMachine Learning EngineerDesign User Embedding Semantic Search
This question evaluates competency in ML system design for recommendation and semantic retrieval, covering user and listing representation learning, two-stage retrieval and ranking architectures, training objectives and label design, bias correction from interaction logs, and evaluation and experimentation practices.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerDesign a Family-Friendly Listing Classifier
This question evaluates proficiency in machine learning system design, including problem formulation tied to product objectives, labeling strategy, feature engineering, model selection, evaluation metrics, robustness to adversarial labels, fairness considerations, and cold-start handling.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerGenerate Customer-Support Instructions from Company Policies
Design a policy-grounded support RAG system with ordered instructions, applicability checks, procedural evaluation, citations, and version-aware debugging.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerRank Home Search Results Without a Text Query
Design a home-search ranker without query text, covering structured intent, training bias, cold start, online evaluation, and production debugging.
ML System DesignOnsite - AnthropicMediumMachine Learning EngineerDesign a model downloader
This question assesses the architectural design of a distributed, secure, and resilient system capable of deploying large-scale machine learning models to heterogeneous edge devices.
ML System DesignOnsiteSystem DesignMLOpsDistributed Systems - AnthropicHardMachine Learning EngineerDesign an inference routing and scheduling layer
This question evaluates system-design and production machine learning engineering skills, specifically distributed systems, routing and scheduling, multi-tenant isolation, caching, dynamic batching, and capacity planning for heterogeneous GPU/CPU inference backends.
ML System DesignOnsite - AppleMediumMachine Learning EngineerDesign App Store search
This question evaluates a candidate's ability to design scalable, low-latency app marketplace search systems, covering competencies in query understanding, metadata ingestion and indexing, candidate retrieval and ranking, personalization, multilingual and typo-tolerant handling, cold-start and spam prevention, as well as online experimentation and monitoring. Commonly asked in the ML system design/search engineering domain to probe architectural trade-offs, measurement of product requirements and success metrics, and operationalization, it spans both high-level conceptual reasoning and practical application details.
ML System DesignOnsite - AppleMediumMachine Learning EngineerDesign a grounded voice assistant
This problem evaluates your ability to design a retrieval-augmented generation (RAG) system for a voice assistant, focusing on grounding model outputs in trusted sources to prevent hallucinations.
ML System DesignOnsiteRAGLLMSystem Design - AppleMediumMachine Learning EngineerOptimize image filters on device
This question evaluates competency in on-device machine learning and performance engineering, covering algorithm choice, numerical precision trade-offs, hardware utilization (CPU/GPU/accelerator), memory layout, tiling/streaming, and trade-offs among latency, memory overhead, and battery impact.
ML System DesignOnsite - AtlassianMediumMachine Learning EngineerDesign Jira bug-to-team classification system
This question tests the ability to design an end-to-end ML pipeline for classifying incoming Jira tickets to appropriate engineering teams based on textual and historical metadata.
ML System DesignOnsiteNLPClassificationScalability - AtlassianMediumMachine Learning EngineerDesign a Jira+Confluence RAG assistant
This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.
ML System DesignOnsite - AtlassianHardMachine Learning EngineerDesign an ML-powered search system
This question tests the ability to design a scalable, low-latency, and relevant search system for internal enterprise documents, focusing on the integration of hybrid search (keyword + semantic) and ranking strategies.
ML System DesignOnsiteInformation RetrievalRankingEmbeddings - AtlassianMediumMachine Learning EngineerDesign an internal data-access chatbot
This question evaluates a candidate's ability to design production-ready ML systems that integrate retrieval-augmented generation, secure access control, tool-based workflows, data privacy, auditability, and low-latency interaction.
ML System DesignOnsite - Capital OneMediumMachine Learning EngineerDesign model deployment, monitoring, and low-latency inference
This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.
ML System DesignOnsite - CohereMediumMachine Learning EngineerDesign How an LLM Detects Questions Beyond Its Training Cutoff and Says So
Design how a large language model recognizes questions that depend on information after its training cutoff and tells the user where its knowledge ends. The question tests data construction for calibrated abstention, prompt versus fine-tuning versus retrieval trade-offs, and evaluating both over-refusal and hallucination.
ML System DesignOnsite - CohereMediumMachine Learning EngineerDesign a Post-Training Pipeline to Improve a 7B Model's Coding for an Enterprise
Design an end-to-end post-training pipeline that improves a 7B language model's coding ability for an enterprise customer, covering data collection, SFT, RLHF, evaluation, serving and a production feedback loop. The question probes dataset construction, reward model choice and why RLHF is still needed when SFT can learn from preference data.
ML System DesignOnsite - CohereMediumMachine Learning EngineerImprove LLM reasoning for a domain task
This problem tests your ability to design a robust pipeline for an LLM-based system that requires multi-step reasoning in a specialized domain (e.g., technical legal analysis or medical diagnosis).
ML System DesignOnsiteLLMReasoningRAG - CreditkarmaMediumMachine Learning EngineerDesign a Revenue Ranking Platform
This problem tests your ability to design a multi-stage ranking system that balances business revenue goals with user relevance, specifically addressing sparse conversion data and long-term user retention.
ML System DesignOnsiteRankingRecommendationMulti-task Learning - 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 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 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 - 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 - 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 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 - FireworksMediumMachine Learning EngineerRecommend Connections Through Mutual Friends and Evaluate the Ranking
Design a connection recommendation system based on mutual connections, define bounded graph affinity scores, and evaluate the ranking pipeline using metrics like NDCG with independent labels.
ML System DesignOnsiteGraph MLRecommender SystemsRanking - GleanMediumMachine Learning EngineerDesign Employee-to-Employee Distance
This question evaluates competencies in ML system design, similarity and representation modeling, feature and data engineering, scalable low-latency serving architectures, and privacy, fairness, and evaluation practices.
ML System DesignOnsite
Adobe
Airbnb
Anthropic
Apple
Atlassian
Capital One
Cohere
Databricks
Faire
Figma
Glean