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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
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- 546
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- 11
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- 8
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Showing 1–15 of 15 results
- AnthropicMediumMachine Learning EngineerExplain multithreading vs multiprocessing
This question tests your conceptual understanding of how process-based and thread-based concurrency models leverage hardware resources and their respective trade-offs in the context of Python and high-performance ML workloads.
Software Engineering FundamentalsOnsiteConcurrencyPythonOperating Systems - Capital OneEasyMachine Learning EngineerHow would you improve card-type detection?
This question tests your ability to design a modular and maintainable classification system that identifies credit card networks (e.g., Visa, Mastercard) based on raw input data while considering validation and error handling.
Software Engineering FundamentalsOnsiteData ValidationString ManipulationSystem Design - 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 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 - FurtheraiMediumMachine Learning EngineerBuild a Create/Execute Workflow Service With an AI Assistant: Plan, Implement, Verify
A 45-minute practical coding exercise: build a small service with create-workflow and execute-workflow APIs while using an AI coding assistant. It evaluates planning, implementing and verifying with the assistant, along with validation of workflow definitions, dependency-ordered execution and step failure handling.
Software Engineering FundamentalsOnsite - GleanMediumMachine Learning EngineerAI-Paired Rate-Limited Wikipedia Crawler That Prioritizes Unseen Title Initials
Pair with an AI coding assistant to build a rate-limited Wikipedia crawler that first crawls pages whose title initial has not yet been covered, then crawls randomly once all 26 letters are done. Tests frontier design, link normalization, rate limiting and disciplined use of AI-generated code.
Software Engineering FundamentalsOnsite - LilaMediumMachine Learning EngineerDebug a Transformer bug in an unfamiliar repo
This question tests your ability to identify and rectify a logical error in a Transformer's multi-head self-attention mechanism, specifically focusing on tensor dimensionality, causality masking, and numerical stability.
Software Engineering FundamentalsOnsiteTransformersDeep LearningDebugging - MicrosoftMediumMachine Learning EngineerExplain a project deeply
This question assesses the candidate's depth of ownership over a previous machine learning project, specifically testing their ability to articulate system design choices, technical trade-offs, and operational resilience.
Software Engineering FundamentalsOnsiteSystem DesignMLOpsArchitecture - 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 - 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 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 - SnowflakeMediumMachine Learning EngineerDesign and Implement a Rate Limiter with an AI Assistant, Then Defend and Test It
Design and implement a rate limiter in an AI-assisted coding round: choose and defend an algorithm against its alternatives, direct an AI assistant to write the code, review it and test it. It tests rate-limiting algorithm trade-offs, concurrency, time handling and critical review of generated code.
Software Engineering FundamentalsOnsite - Startups.ComMediumMachine Learning EngineerDebug and scale a PyTorch training loop
This question evaluates debugging and performance engineering skills for deep learning training pipelines, including diagnosing convergence and instability issues in PyTorch, scaling training with Fully Sharded Data Parallel (FSDP), designing sparse gradient all-reduce communication patterns, and judging when to implement custom CUDA/Triton kernels. Commonly asked for Machine Learning Engineer roles within the Software Engineering Fundamentals domain, it assesses both conceptual understanding of numerical stability and distributed systems and practical application skills in configuring multi-GPU training, communication trade-offs, and low-level performance and correctness validation.
Software Engineering FundamentalsOnsite - xAIMediumMachine Learning EngineerDynamic Batching Decode Loop for a Mock Language Model with Slot Refilling
An LLM inference coding question: write the generation loop for a mock language model that returns one next token per sequence in a batch. Requests finish at different steps on a stop token or a max-token limit, and freed batch slots must be refilled from a waiting queue, testing state tracking and batching logic.
Software Engineering FundamentalsOnsite - xAIMediumMachine Learning EngineerFind All Faulty GPU Nodes with Parallel Group Tests That Cannot Share Nodes
An algorithm design question about finding every faulty GPU node when the only tool is a group test that fails if any node in the set is bad. Tests need at least two nodes and parallel tests cannot share nodes, so it tests adaptive testing strategies, identifiability, and cost in rounds and tests.
Software Engineering FundamentalsOnsite
Anthropic
Capital One
Cohere
Glean
Microsoft
OpenAI
Snowflake
xAI