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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 31–60 of 178 results
- 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 Personalized Promotion Recommendations
This question tests the ability to design an end-to-end recommendation system for personalized financial promotions, focusing on handling sparse conversion data, real-time feature requirements, and feedback loops.
ML System DesignTechnical ScreenRecommendation SystemsRankingFeature Engineering - 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 - DRWMediumMachine Learning EngineerBuild pipeline for imbalanced classification
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 Build pipeline for imbalanced classification states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment - DRWHardMachine Learning EngineerTrain LinearSVC to beat a hidden baseline
Train LinearSVC to beat a hidden baseline 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 DesignOnline Assessment - 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 - 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 - 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 - Freddie MacMediumMachine Learning EngineerImprove Trust in a RAG System
This question evaluates system-design competency for trustworthy retrieval-augmented generation, probing skills in retrieval and generation architecture, confidence scoring and calibration, evidence verification and fallback mechanisms, monitoring, privacy and compliance, human feedback integration, and adversarial robustness.
ML System DesignTechnical Screen - 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 - GleanMediumMachine Learning EngineerDesign a Work-Together Distance Between Any Two Employees of a Company
Design a learned distance between any two employees of a company that reflects how much they actually work together. Tests ML framing without ground-truth labels, collaboration signals and their normalization, link-prediction training, evaluation, privacy and serving at company scale.
ML System DesignOnsite - GoogleMediumMachine Learning EngineerDesign ML system for self-driving perception
This question assesses the ability to architect a real-time perception system for autonomous vehicles, focusing on sensor integration, model optimization for edge deployment, and safety-critical failure handling.
ML System DesignTechnical ScreenComputer VisionSensor FusionEdge ML - GoogleMediumMachine Learning EngineerDesign a Product or Video Recommendation System
This question evaluates competency in designing large-scale recommendation systems, encompassing machine learning modeling, candidate generation and ranking, data engineering, online serving, feedback-driven learning, evaluation, and experimentation.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign a reaction-factor prediction system
This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign a real-time recommendation system
This question evaluates a candidate's competency in designing large-scale, low-latency real-time recommendation systems, covering feature engineering and feature-store consistency, candidate generation and ranking architectures, cold-start handling, latency versus accuracy trade-offs, monitoring, and operational scalability.
ML System DesignOnsite - GoogleMediumMachine Learning EngineerDesign a watch-next video recommender and derive its losses and optimizer update rules
Design the watch-next recommendations shown while a user watches a video, from objectives and candidate generation to ranking, serving and evaluation. The interview probes modeling depth: exact loss functions for retrieval and multi-task ranking, the gradient at the output logit, and the update rules of the optimizers used to train each model.
ML System DesignOnsite - GoogleHardMachine Learning EngineerDesign feedback-driven recommender
Design feedback-driven recommender 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 DesignOnsite - GoogleHardMachine Learning EngineerDesign multi-GPU matrix multiplication
This question evaluates proficiency in multi-GPU parallelism and system-level ML engineering, covering data partitioning, inter-GPU communication primitives, compute scheduling and overlap, memory layout and buffer reuse, numerical precision trade-offs, synchronization, scalability, and failure handling.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerExplain ML model fundamentals
Explain ML model 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 DesignOnsite - GoogleHardMachine Learning EngineerExplain Pipeline Parallelism in GRPO Training
Design and analyze an efficient pipeline parallel training architecture for Group Relative Policy Optimization (GRPO) on large reasoning models. Tests deep understanding of pipeline scheduling, microbatching, distributed advantage normalization, and policy staleness trade-offs.
ML System DesignTechnical ScreenPipeline ParallelismRLHF / GRPODistributed Training - HebbiaHardMachine Learning EngineerDesign a Multi-Agent Document Search and Visualization Product
This problem tests your ability to design a complex multi-agent orchestration system that integrates large-scale document retrieval with reliable citation tracking and a user-facing visualization interface.
ML System DesignOnsiteMulti-Agent SystemsRAGOrchestration - Jane StreetHardMachine Learning EngineerDesign an end-to-end training framework
Design an end-to-end training framework 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
Cohere
DRW
Databricks
DoorDash
Faire
Figma
Freddie Mac
Glean
Google
Jane Street