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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 241–270 of 782 results
- HarveyMediumMachine Learning EngineerTraining LLM Agents for Long-Context, Long-Horizon Tasks: Environments and Rewards
An ML research discussion on training language-model agents for long-context, long-horizon tasks. It tests the training recipe from context extension through supervised trajectories to RL, the design of reproducible training environments, and reward design that resists reward hacking over long trajectories.
Machine LearningOnsite - 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 - HubSpotMediumMachine Learning EngineerDiscuss compensation expectations and level
This question evaluates a candidate's ability to articulate compensation expectations, total rewards understanding (base, bonus, equity, sign-on) and flexibility, along with professional communication and self-awareness, and is categorized under Behavioral & Leadership and HR screening for compensation negotiation.
Behavioral & LeadershipHR Screen - Hudson River TradingEasyMachine Learning EngineerCount People Reaching the Boundary
This question evaluates algorithmic simulation and event-driven modeling skills, specifically handling discrete versus continuous time, visibility constraints relative to a stationary watcher that changes facing direction, and boundary conditions for state transitions.
Coding & AlgorithmsOnsite - InceptionMediumMachine Learning EngineerImplement Autoregressive Decoding in PyTorch
This question evaluates a candidate's ability to implement autoregressive decoding and sampling strategies in PyTorch, testing competencies in sequence generation, probabilistic sampling methods (greedy, temperature, top-k, top-p), batching, and end-of-sequence handling.
Machine LearningTechnical Screen - IndeedMediumMachine Learning EngineerImplement MSE Loss and Linear Regression from Scratch
Implement the mean squared error loss and a linear regression model with fit and predict methods, written from scratch rather than with a library model. It tests deriving and coding the gradients, choosing a training method, feature scaling, input validation, and verifying the implementation.
Machine LearningTechnical Screen - InstacartHardMachine Learning EngineerExplain Core ML Concepts
This question tests the conceptual understanding of supervised learning foundations, the mathematical relationship between regression techniques, and the convergence behavior of optimization algorithms.
Machine LearningTechnical ScreenLinear RegressionLogistic RegressionOptimization - InstacartHardMachine Learning EngineerSolve Two Sorted-Array Tasks
This problem tests your ability to efficiently merge and find elements in sorted arrays, focusing on optimal time complexity and edge case handling.
Coding & AlgorithmsTechnical ScreenArraysBinary SearchTwo Pointers - IntuitMediumMachine Learning EngineerLongest Meeting-Free Business-Hours Block per Employee and Day
Find each employee’s longest meeting-free business-hours interval using clipped attendance, merged overlaps, complete day coverage, and SQL window logic.
Data Manipulation (SQL/Python)Technical Screen - J.P. MorganMediumMachine Learning EngineerDesign an Entitlement-Aware Agentic Portfolio Workflow
This problem tests the ability to architect an LLM-based agentic system that balances autonomous task execution with strict role-based access control (RBAC) and auditability.
System DesignTechnical ScreenAgentic WorkflowSecurityLLM - J.P. MorganMediumMachine Learning EngineerReturn Every Index Pair Whose Values Sum to a Target
This problem tests your ability to handle frequency counts and index tracking using hash maps to identify all possible index pairs that sum to a specific target, particularly in the presence of duplicate values.
Coding & AlgorithmsTechnical ScreenHash MapArraysAlgorithms - Jane StreetMediumMachine Learning EngineerBoard Simulation: Bottom-Insert Pieces with Uniform Row and Column Detection
This question evaluates the ability to simulate a 2-D game board and maintain dynamic state invariants, testing array manipulation, state-tracking, and detection of uniform rows and columns under bottom-insert semantics.
Coding & AlgorithmsTechnical Screen - Jane StreetHardMachine Learning EngineerBuild a time-series forecasting model
Build a time-series forecasting model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - Jane StreetMediumMachine Learning EngineerCode Editor with Block Shrink and Expand (Code Folding)
This question evaluates a candidate's ability to model and manipulate indentation-based hierarchical structures, maintain nested visibility state for collapsed blocks, and map between original file lines and dynamic display numbering.
Coding & AlgorithmsTechnical Screen - Jane StreetMediumMachine Learning EngineerConnect-N on an Infinite Board with Gravity
This question evaluates implementation skills for efficient algorithms and data structures to manage a sparse, unbounded grid with gravity and to detect N-in-a-row runs both vertically and horizontally.
Coding & AlgorithmsTechnical Screen - 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 - Jane StreetHardMachine Learning EngineerDesign and implement a tiny language runtime
Design and implement a tiny language runtime evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetHardMachine Learning EngineerImplement compiler for custom language
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Implement compiler for custom language states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Jane StreetEasyMachine Learning EngineerMarketplace Data Store: Order Matching and End-of-Day Reconciliation
This question evaluates understanding of stateful data structures, deterministic order-matching logic, and time-ordered reconciliation of transaction logs, focusing on correct handling of pricing, tie-breaking, and re-opened inventory.
Coding & AlgorithmsTechnical Screen - Jane StreetHardMachine Learning EngineerPredict future time-series values
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 Predict future time-series values states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - Jump TradingMediumMachine Learning EngineerSolve stock trading with pattern rules
This set of problems evaluates algorithmic skills in pattern recognition, state management, and sequence processing, specifically testing pattern-based decision rules on time-series stock data and iterative substring elimination in string processing within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - Jump TradingMediumMachine Learning EngineerSolve stock-pattern trading and substring elimination
This question evaluates algorithmic skills in array pattern recognition and string processing, specifically detecting day-to-day sign patterns for simulated trading and repeated elimination of fixed two-character substrings; it belongs to the Coding & Algorithms domain for a Machine Learning Engineer position and targets practical application of linear-time, constant-extra-space techniques. It is commonly asked to evaluate efficient O(n) algorithm design, handling of stateful streaming simulations and in-place string reduction under tight time/space constraints, and correctness reasoning for edge cases.
Coding & AlgorithmsOnline Assessment - LiftoffHardMachine Learning EngineerProbability That the Sum of 1,000 Fair Die Rolls Exceeds a Threshold
A fair six-sided die is rolled 1,000 times; estimate the probability that the sum of the faces exceeds a threshold, including a threshold of 600. Tests the mean and variance of one roll, how they scale for a sum of independent rolls, the normal approximation with a continuity correction, and range checks.
Statistics & MathOnsite - 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 - LilaMediumMachine Learning EngineerDesign a DNA-sequence optimization loop
This problem tests your ability to design a closed-loop active learning system for protein or DNA sequence engineering, focusing on the iterative cycle of sequence generation, uncertainty estimation, and experimental validation.
ML System DesignOnsiteActive LearningGenerative ModelsBioinformatics - LinkedInMediumMachine Learning EngineerAnswer practical ML foundations questions
This question evaluates an interviewee's practical mastery of ML foundations, covering probability calibration and its evaluation, feature selection methods including neural-network approaches, tokenizer types and trade-offs in NLP, and optimizer behavior such as Adam's maintained statistics.
Machine LearningOnsite - LinkedInEasyMachine Learning EngineerCompute point-to-segment minimum distance
This problem tests your ability to apply vector projections and geometric constraints to find the shortest distance from a point to a line segment in a 2D plane.
Coding & AlgorithmsTechnical ScreenGeometryVectorsMath - LinkedInMediumMachine Learning EngineerCompute total covered interval length
Compute total covered interval length evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - LinkedInMediumMachine Learning EngineerDesign LinkedIn Learning course recommendations
The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.
ML System DesignOnsite - LinkedInHardMachine Learning EngineerDesign a Model That Predicts Whether a Member Clicks a Displayed Job
An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.
ML System DesignTechnical Screen
Harvey
HubSpot
Hudson River Trading
Indeed
Instacart
Intuit
J.P. Morgan
Jane Street
Liftoff
LinkedIn