practice a little every day, paws crossed ♡
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 61–90 of 351 results
- Capital OneMediumMachine Learning EngineerValidate virtual credit card transactions from encoded IDs
This question evaluates understanding of virtual credit card product trade-offs and the ability to design data models and deterministic validation logic for encoded transaction attributes, testing competencies in payment systems, data encoding/decoding, and rule-based validation.
System DesignOnsite - CohereMediumMachine Learning EngineerBuild an LLM-driven text-game agent
This question tests the ability to design a robust pipeline for an LLM-based agent interacting with a stateful environment, focusing on context management, output validation, and error recovery.
Software Engineering FundamentalsOnsiteLLMPrompt EngineeringSystem Design - CohereMediumMachine Learning EngineerDefend Your Own Research Paper: Experimental Setup, Practicality and Later Work
A research paper round where you present your own published paper to an interviewer who already knows it and questions its weaknesses. Prepare to judge whether the experimental setup was reasonable, whether the results are practical, and what the field has learned since publication, all with specific, honest self-critique.
Machine LearningOnsite - 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 EngineerExplain LLM training, RL, and evaluation
This question tests your conceptual understanding of the end-to-end LLM lifecycle, specifically the transition from raw text pre-training to alignment and evaluation.
Machine LearningOnsiteLLMRLHFPPO - CohereMediumMachine Learning EngineerExplain Top-k and Top-p Decoding
This question tests your conceptual and mathematical understanding of Top-k and Top-p (nucleus) sampling, focusing on how they modify the output probability distribution of autoregressive language models to balance creativity and coherence.
Machine LearningOnsiteNLPGenerative AISampling - CohereMediumMachine Learning EngineerImplement Autoregressive Decoding Strategies
This problem tests your ability to implement common sequence decoding strategies used in large language models by manipulating token probability distributions.
Coding & AlgorithmsOnsiteLLMSamplingSoftmax - 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 - CohereMediumMachine Learning EngineerReplace a Heuristic Terminal Text Game's Python Logic With LLM Calls
A live coding round: replace the heuristic Python implementation of a terminal text game with one driven by an LLM while keeping the game playable end to end. AI assistants are allowed, but the round judges your own implementation and debugging, including validating model output, fallbacks, and tests with a fake model.
Software Engineering FundamentalsOnsite - CoinbaseMediumMachine Learning EngineerBuild a baseline classification model from messy data
This question evaluates skills in practical machine learning engineering, including data cleaning, preprocessing, feature selection, handling mixed numeric and categorical features, and baseline model construction for a binary classification task.
Machine LearningOnsite - CoinbaseMediumMachine Learning EngineerWrite SQL to rank top products per category
This question evaluates SQL proficiency in aggregation, time-based filtering, joining transactional and reference tables, and ranking/windowing techniques to compute and deterministically order product revenue with tie-breaking by product_id.
Coding & AlgorithmsOnsite - 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 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 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 - 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 - 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 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 - 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
Capital One
Cohere
Coinbase
Databricks
DoorDash
Faire
Figma