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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 331–360 of 381 results
- UberMediumMachine Learning EngineerDescribe an ML system you built
This question evaluates end-to-end machine learning system design and ownership — covering problem framing, data and feature engineering, model choice, training and evaluation, deployment, monitoring, and measured impact — and it probes interpersonal leadership in handling conflicts over model decisions, metrics, or product trade-offs.
Behavioral & LeadershipTechnical Screen - UberMediumMachine Learning EngineerDesign a feed ranking system
This question evaluates a candidate's competency in designing end-to-end machine-learning-based feed ranking systems, including setting ranking objectives, candidate generation and filtering, feature design, model training and serving, and operational concerns like freshness, diversity, and scalability.
ML System DesignTechnical Screen - UberEasyMachine Learning EngineerExplain and test completion-rate gaps
This question evaluates understanding of causal inference, product-metrics analysis, experimental design, and statistical hypothesis testing (including two-sample t-tests) as applied to operational problems in a marketplace.
Machine LearningTechnical Screen - UberHardMachine Learning EngineerFind Robots by Nearest-Blocker Distances
Implement an algorithm to identify all robots in a grid whose four-directional distances to the nearest obstacle or grid boundary match a target query.
Coding & AlgorithmsTechnical Screenmatrixprefix-sumhash-table - UberMediumMachine Learning EngineerFind a Black-Box Convex Function Minimum
This question evaluates proficiency in zero-order optimization and algorithm design, focusing on reasoning about convexity and unimodality when only function evaluations are available.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerFind robots in a grid whose four-direction distances to blockers match a query
Given a grid of robots, blockers and empty cells plus a query of four distances, return every robot whose distances to the nearest blocker or grid edge going up, down, left and right match the query exactly. Tests precise distance definitions, boundary handling and scaling past a per-robot scan.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerImplement 1D convex minimization in Python
An Uber Machine Learning Engineer technical-screen question on derivative-free 1D convex optimization: minimize a black-box convex function on a closed interval using only function evaluations. It tests method selection (golden-section vs ternary search), stopping criteria and tolerances, function-evaluation and time/space complexity analysis, and robust handling of flat regions, boundary optima, noisy evaluations, and numerical precision.
Machine LearningTechnical Screen - UberMediumMachine Learning EngineerImplement CLIP Contrastive Loss
This question evaluates understanding and implementation of contrastive representation learning concepts—specifically similarity matrices, symmetric image-text contrastive loss, normalization, temperature scaling, and label construction—testing competency in building losses for embedding alignment in the Machine Learning domain.
Machine LearningTechnical Screen - UberMediumMachine Learning EngineerImplement Multi-Head Self-Attention
This question tests the ability to implement the multi-head self-attention mechanism from scratch using PyTorch, focusing on tensor manipulation, linear projections, and the scaled dot-product attention formula.
Machine LearningTechnical ScreenPyTorchDeep LearningTransformers - UberMediumMachine Learning EngineerImplement linear and logistic regression
This question evaluates understanding and hands-on implementation of linear and logistic regression, testing competencies in prediction functions, loss formulation, gradient computation, gradient descent training, regularization, and model selection within the Machine Learning domain (supervised learning and statistical modeling).
Machine LearningTechnical Screen - UberMediumMachine Learning EngineerPresent a Marketplace ML Project Deep Dive
This question evaluates a candidate's competence in end-to-end ML system design, product-oriented machine learning, and cross-functional skills such as feature engineering, model deployment, online experimentation, monitoring, stakeholder communication, and measuring business impact.
ML System DesignTechnical Screen - UberMediumMachine Learning EngineerWhere to apply ML in a task flow from customer request to driver-uploaded evidence
An ML system design discussion about a task platform: walk the end-to-end flow from a customer submitting a task to a driver completing it and uploading evidence, and identify where machine learning and LLMs add value. Tests prioritization, model framing, labels, evaluation and risk handling.
ML System DesignTechnical Screen - UiPathMediumMachine Learning EngineerCompute Minimum L-Moves on Infinite Grid
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute Minimum L-Moves on Infinite Grid states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - UiPathEasyMachine Learning EngineerExplain Core ML Fundamentals
This question tests your conceptual understanding of how fundamental components of neural networks and predictive models affect optimization, generalization, and performance.
Machine LearningTechnical ScreenGradient DescentRegularizationActivation Functions - UiPathMediumMachine Learning EngineerExplain Transformer Layers and FFN Rationale
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Transformer Layers and FFN Rationale states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerImplement cocktail shaker sort and analyze
This question evaluates a candidate's understanding of sorting algorithms and algorithmic optimization, specifically implementation and enhancement of cocktail shaker sort, plus analysis of time and space complexity, stability, and counts of comparisons and swaps.
Coding & AlgorithmsTechnical Screen - UiPathMediumMachine Learning EngineerLLM fundamentals: Transformer internals, beam search decoding, and LoRA fine-tuning
An LLM fundamentals question that asks you to explain Transformer internals such as scaled self-attention, multi-head attention, positional information and causal masking. It also covers how beam search decoding compares with greedy decoding and sampling, and how LoRA reduces the cost of fine-tuning.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerProbability a draw-until-threshold score ends at or below a limit
A coding question on probability: a player repeatedly adds a uniformly random number of points until the total reaches a threshold, and you return the probability that the final total is at most a given limit. It tests careful probabilistic reasoning and an efficient computation for inputs up to ten thousand.
Coding & AlgorithmsTechnical Screen - UiPathMediumMachine Learning EngineerReliable LLM tool calling: format control, failure recovery, and runaway loops
An LLM agent question on tool calling: how a model invokes tools, how to keep tool calls in a valid format, how to make tool use stable and fix wrong calls, and how to handle tool-call loops that run too long. It tests practical reliability engineering for production agents.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerWalk through resume and handle ambiguity
This question evaluates a candidate's ability to communicate technical career history, quantify project impact, and demonstrate leadership and ambiguity-handling skills relevant to a Machine Learning Engineer role.
Behavioral & LeadershipTechnical Screen - VoleonMediumMachine Learning EngineerAudit and Extend a Time-Series ML Pipeline
Audit a time-series ML pipeline for leakage, tune with chronological validation, and add inference with saved preprocessing and timestamp-correct features.
Machine LearningTechnical Screen - VoleonHardMachine Learning EngineerBuild a Tool-Using Weather Assistant
Design a grounded weather assistant with typed model tool calls, server-side credentials, validated live data, provenance, and safe failure behavior.
Software Engineering FundamentalsTechnical Screen - VoleonHardMachine Learning EngineerDesign a cold-start video recommender
This problem tests the ability to architect a recommendation system that provides personalized content for new users or items with zero historical interaction data.
ML System DesignTechnical ScreenRecommender SystemsCold StartMulti-Armed Bandits - VoleonHardMachine Learning EngineerEstimate Fine-Tuning Memory Requirements
Learn to estimate full and parameter-efficient fine-tuning memory with a transparent ledger for model state, activations, sharding, and peak overhead.
ML System DesignTechnical Screen - VoleonHardMachine Learning EngineerExplain ML and statistical modeling
This problem tests your theoretical understanding of statistical modeling and machine learning, specifically focusing on the implications of loss function selection, handling heteroskedasticity, and the trade-offs between bias and variance in predictive modeling.
Machine LearningTechnical ScreenStatisticsRegressionOptimization - VoleonHardMachine Learning EngineerExplain a Markov Decision Process
Review the components and trajectory semantics of an MDP, including state sufficiency, policies, transitions, rewards, and discounted return.
Machine LearningTechnical Screen - VoleonHardMachine Learning EngineerExplain the Bellman Equation
Understand Bellman expectation and optimality equations through one-step return decomposition, fixed points, and reinforcement-learning algorithms.
Machine LearningTechnical Screen - VoleonHardMachine Learning EngineerFine-Tune a Language Model on Question-Answer Pairs
Review the full workflow for supervised language-model fine-tuning on question-answer pairs, including masking, leakage control, evaluation, and rollout.
Machine LearningTechnical Screen - WaymoMediumMachine Learning EngineerDebug Tensor Conversion Bugs
This question evaluates debugging and implementation-level skills in tensor and array manipulation, focusing on aliasing behavior, conversion between column lists and ndarrays, correct handling of non-divisible chunking, and writing tests for these issues.
Coding & AlgorithmsTechnical Screen - WaymoMediumMachine Learning EngineerDesign Large-Scale Inference Serving
This question evaluates understanding of large-scale ML inference systems, assessing competencies in capacity planning, latency and tail-latency engineering, memory and bandwidth estimation, hardware selection (CPUs/GPUs/specialized accelerators), batching and caching trade-offs, and reliability concerns such as out-of-memory prevention and recovery. It is commonly asked to test practical system-design skills for production deployment by requiring back-of-the-envelope QPS and resource estimates and reasoning about operational trade-offs; this belongs to the ML system design category and emphasizes practical application over purely conceptual theory.
ML System DesignTechnical Screen
Uber
UiPath
Voleon
Waymo