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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 181–210 of 782 results
- FigmaHardMachine Learning EngineerEnd-to-End ML Project Deep Dive: Decisions, Evaluation Design and Career Goals
A screening question for an ML engineer role focused on post-training for agents. It asks you to present an end-to-end project you owned, defend its technical decisions and strategy, explain how you designed and ran its evaluation, and say why you are leaving your current company and where your career is heading.
Behavioral & LeadershipTechnical Screen - FigmaMediumMachine Learning EngineerImplement Layer History and Grid Counting
This question tests state management through a Command pattern implementation for undo/redo operations and spatial reasoning using depth-first search (DFS) to identify connected components in a 2D grid.
Coding & AlgorithmsOnsiteDesign PatternsStackGraphs - 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 EngineerDescribe a Machine Learning Project
This question evaluates a candidate's ability to lead end-to-end machine learning projects, emphasizing technical ownership, system design, data strategy, model selection, deployment, and stakeholder management.
Behavioral & LeadershipTechnical Screen - 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 - 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 - FurtheraiMediumMachine Learning EngineerCutting Scope to Ship the Core Customer Problem First: Working Pragmatically With Product
A behavioral question about working pragmatically with a product team: describe a real time you cut scope to ship a minimal first version that solved the customer's core problem, and explain how you and a product manager decide what goes into a first release, what waits, and how disagreements are resolved.
Behavioral & LeadershipOnsite - FurtheraiMediumMachine Learning EngineerMap an Insurance Underwriting Workflow and Pick What AI Automation Should Fix First
A business-curiosity question for an engineer at a company automating insurance underwriting: describe the underwriting workflow from submission to decision, decide which pain points AI should automate first and how to measure the result, and ask questions that show genuine interest in the customer's work.
Product Design & StrategyOnsite - FurtheraiMediumMachine Learning EngineerMerge Overlapping Closed Intervals Into a Sorted Disjoint List
A coding question that merges an unsorted list of closed integer intervals into disjoint intervals sorted by start. Intervals that overlap or merely touch at an endpoint must be combined, so it tests exact endpoint semantics, transitive merges, and nested or duplicate intervals.
Coding & AlgorithmsOnsite - FurtheraiMediumMachine Learning EngineerMotivation for an Early-Stage AI Startup, Hands-On AI Experience, and Startup Fit
A fit conversation for a staff applied AI engineering role at an early-stage startup: explain your relevant projects and hands-on experience with AI, why you are looking now, why an early-stage company, and what startup experience you bring, as probed by a hiring manager and a CTO.
Behavioral & LeadershipOnsite - FurtheraiMediumMachine Learning EngineerWalk Through an AI Project You Built: Decisions, Alternatives, Trade-offs, Impact
A behavioral deep-dive question asking a senior candidate to walk through a project where AI was central, covering the problem, the technical difficulty, architecture decisions, alternatives considered, trade-offs and lessons, and measured business or customer impact, with close probing of the alternatives and trade-offs.
Behavioral & LeadershipOnsite - GatikHardMachine Learning EngineerDesign a YAML-Configured Point-Cloud Transformation Pipeline
Tests software engineering fundamentals and clean object-oriented design in Python by building an extensible, schema-validated point-cloud transformation pipeline with registry patterns and metadata tracking.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignPoint CloudData Pipelines - GeneralmotorsHardMachine Learning EngineerCount Islands in a Grid
This problem tests your ability to navigate a 2D grid and identify distinct connected components using graph traversal techniques like Depth-First Search (DFS) or Breadth-First Search (BFS).
Coding & AlgorithmsTechnical ScreenGraphsDFSBFS - 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 - GleanMediumMachine Learning EngineerDecide If a Passenger Can Reach a Station Using Time-Indexed Train Routes and Transfers
Given trains described as time-indexed lists of stations, decide whether a passenger starting at one station at time zero can reach another by riding, waiting and transferring. Tests modeling station and time as search states, handling waits and same-time transfers, and breadth-first search over a time-expanded graph.
Coding & AlgorithmsOnsite - 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 - GleanMediumMachine Learning EngineerDetermine Reachability in Train Schedule
Solve train-schedule reachability with waiting and transfers using time-respecting graph search. The answer models earliest station arrival, preprocesses station visits, handles forward-only train routes, and explains transfer edge cases and complexity.
Coding & AlgorithmsTechnical Screen - GleanMediumMachine Learning EngineerFind Earliest Train Route
This question evaluates a candidate's ability to model and search time-dependent transit networks, combining temporal graph reasoning, state-space search, and path reconstruction for itinerary reporting.
Coding & AlgorithmsOnsite - GleanMediumMachine Learning EngineerImplement Rate-Limited Wikipedia Crawler
This question evaluates a candidate's ability to implement a rate-limited web crawler, testing skills in crawl frontier management, prioritization by page-title initial, deduplication, rate limiting, and robust error handling.
Coding & AlgorithmsOnsite - Goldman SachsHardMachine Learning EngineerComplete decision tree and gradient descent functions
This question evaluates implementation skills in supervised machine learning and numerical optimization, specifically computing Gini impurity and selecting optimal binary splits for a decision tree alongside deriving and applying a single-step gradient descent update for linear regression.
Coding & AlgorithmsOnline Assessment - Goldman SachsHardMachine Learning EngineerEvaluate TPR/FPR, sigmoid, and activations
This question evaluates competency in binary classification metrics (confusion matrices, TPR/FPR), basic neural network forward computation (sigmoid neuron z and output), and knowledge of activation function selection and pitfalls.
Machine LearningOnline Assessment - GoogleMediumMachine Learning EngineerAnswer conflict, failure, and proud project questions
This question evaluates a Machine Learning Engineer's behavioral competencies such as conflict resolution, accountability, communication, leadership, and the ability to quantify impact by eliciting real examples of conflict, failure, and a proud project.
Behavioral & LeadershipTechnical Screen - GoogleHardMachine Learning EngineerCompare Language-Model Post-Training Methods
This question tests your conceptual depth in LLM alignment by asking you to differentiate between supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and group-relative optimization (e.g., GRPO) based on their mathematical objectives and operational characteristics.
Machine LearningTechnical ScreenLLMAlignmentRLHF - GoogleMediumMachine Learning EngineerCompare NLP tokenization and LLM recommendations
This question tests the conceptual understanding of tokenization strategies in NLP and the architectural trade-offs when integrating LLMs into recommendation systems.
Machine LearningOnsiteNLPLLMRecommendation-Systems - GoogleMediumMachine Learning EngineerCompute sum over consecutive-step subarrays
This problem tests your ability to identify contiguous subarrays that follow an arithmetic progression with a common difference of 1 (consecutive steps) and calculate the sum of elements within these subarrays efficiently.
Coding & AlgorithmsOnsiteArraysMathSliding Window - GoogleHardMachine Learning EngineerConstruct connected crop layout and safe paths
This problem tests your ability to design a constructive grid layout that satisfies connectivity and size constraints, followed by pathfinding through dynamic or constrained environments.
Coding & AlgorithmsTechnical ScreenBacktrackingGraph TheoryBFS - GoogleHardMachine Learning EngineerDescribe conflict and failure using STAR framework
This question evaluates interpersonal and leadership competencies such as conflict resolution, accountability, communication, and reflective learning, along with proficiency in structuring responses using the STAR behavioral framework.
Behavioral & LeadershipOnsite - GoogleMediumMachine Learning EngineerDescribe leadership under changing priorities
This question evaluates leadership, ownership, prioritization, communication, and adaptability skills within the Behavioral & Leadership domain for a Machine Learning Engineer role.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerDescribe your proudest project
This question evaluates leadership, communication, and product-focused machine learning engineering competencies, including end-to-end project ownership, technical decision-making, cross-functional collaboration, metrics-driven impact measurement, and risk management; it is categorized as Behavioral & Leadership within the Machine Learning Engineering domain. It is commonly asked to determine how candidates articulate trade-offs, quantify outcomes, justify technical choices, and demonstrate both conceptual understanding and practical application of ML systems, constraints, and tooling in real-world projects.
Behavioral & LeadershipTechnical Screen
Figma
Freddie Mac
Glean
Goldman Sachs
Google