Machine Learning Engineer Interview Questions

Showing 31–60 of 381 results

• AppleMedium·Machine Learning Engineer
  Write a generator for substring pattern matches

  This question evaluates understanding of string pattern matching and generator/iterator design, testing skills in handling overlapping matches, streaming output, and worst-case time complexity considerations.

  Coding & AlgorithmsTechnical Screen
• Applied IntuitionMedium·Machine Learning Engineer
  Debug a GPT training pipeline

  This question evaluates debugging and implementation skills for transformer-based training pipelines, specifically attention masking, training-loop correctness, positional encoding integration, and the ability to design unit tests that catch such bugs.

  ML System DesignTechnical Screen
• Applied IntuitionMedium·Machine Learning Engineer
  Design Ordered CUDA Reduction

  This question evaluates understanding of parallel GPU programming, deterministic reduction ordering, and numerical stability in floating-point arithmetic, within the domain of parallel algorithms, GPU/CUDA programming, and numerical computing for a Software Engineering Fundamentals / Machine Learning Engineer role.

  Software Engineering FundamentalsTechnical Screen
• Applied IntuitionMedium·Machine Learning Engineer
  Implement a Fixed-Capacity Deque

  This question evaluates proficiency with double-ended queue semantics, fixed-capacity data structure design, constant-time (O(1)) operations, and correct handling of boundary conditions and wraparound.

  Coding & AlgorithmsTechnical Screen
• Applied IntuitionMedium·Machine Learning Engineer
  Implement and explain positional encoding

  This question evaluates knowledge of positional encoding mechanisms for Transformer language models, covering embedding mathematics, tensor shapes and broadcasting, PyTorch implementation details, expected training and inference symptoms when positional information is omitted, and methods for empirical verification and ablation.

  Machine LearningTechnical Screen
• Applied IntuitionHard·Machine Learning Engineer
  Validate Nested Configuration Objects

  This question evaluates schema validation skills including recursive data structures, primitive and composite type checking, and structured error reporting for configuration objects, categorized under Coding & Algorithms for a Machine Learning Engineer role and focused on practical implementation.

  Coding & AlgorithmsTechnical Screen
• AuroraHard·Machine Learning Engineer
  Applying LLMs to Autonomous Driving Despite Hallucination and Real-Time Limits

  An open-ended ML system design discussion on how large language models and vision-language models could be used in autonomous driving, including whether one could actually drive the vehicle. It tests mapping off-board and on-vehicle uses, containing hallucination and latency in a safety-critical stack, and planning evaluation.

  ML System DesignTechnical Screen
• AuroraHard·Machine Learning Engineer
  Pitching an LLM-for-Autonomy Initiative to Leadership to Win Scope

  A leadership question asking how you would present an initiative to bring language models into a self-driving program to leadership and win scope for it. It tests problem framing, gathering evidence, handling risk in a safety-critical organization, aligning stakeholders, and making a concrete, phased ask.

  Behavioral & LeadershipTechnical Screen
• AAutodeskMedium·Machine Learning Engineer
  Explain 3D geometry data

  Explain 3D geometry data 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
• BBeaconfireEasy·Machine Learning Engineer
  Check Whether a String Is Built by Repetition

  This problem tests your ability to detect periodic string patterns using string manipulation or mathematical properties, specifically focusing on efficient substring identification.

  Coding & AlgorithmsTechnical ScreenStringsPattern MatchingAlgorithms
• BByteDanceHard·Machine Learning Engineer
  Combined Median of Two Sorted Integer Lists in Logarithmic Time

  From a ByteDance technical screen for a machine learning engineer role: given two sorted integer arrays, return the median of all their elements combined, averaging the two middle values when the total count is even. It tests reasoning about sorted order under a logarithmic time target and edge cases such as an empty array.

  Coding & AlgorithmsTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Compute Sentence Similarity

  This question evaluates understanding of sentence-level and token-level embedding techniques, text preprocessing, similarity metrics, edge-case handling for empty or unknown tokens, and trade-offs between pretrained sentence encoders and averaged word embeddings.

  Machine LearningTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Design Self-Dealing Detection for Marketplaces

  This question evaluates competency in machine learning system design for fraud detection, focusing on modeling buyer–seller–item–transaction relationships, feature engineering across graph, temporal, behavioral and account-linking signals, and production concerns; it is in the ML System Design domain and emphasizes practical application and system-level architecture over purely theoretical concepts. It is commonly asked to assess a candidate’s ability to reason about data collection and labeling, model and rule trade-offs, evaluation metrics, scalability and deployment trade-offs when detecting coordinated or self-dealing activity in marketplace environments.

  ML System DesignTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Design an Enterprise Tool-Using Agent

  This question evaluates a candidate's ability to design production-grade LLM agents that integrate external tools, manage long-running and branching workflows, persist complex state, and ensure safety, observability, and reliable evaluation in enterprise settings.

  ML System DesignTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Explain LLM generational improvements, multimodal LLMs, and normalization in LLMs

  Three conceptual LLM questions: the main improvements across generations of a model family as described in technical reports, how multimodal image-and-text models are built and trained, and why modern LLMs use layer normalization or RMSNorm with pre-norm placement instead of batch normalization.

  Machine LearningTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Explain XGBoost's Overfitting Resistance

  This question evaluates understanding of ensemble learning and model generalization, focusing on why gradient-boosted tree ensembles are often less prone to overfitting than single decision trees.

  Machine LearningTechnical Screen
• BByteDanceHard·Machine Learning Engineer
  Implement a Transformer Decoder from Scratch Without Starter Code

  Implement a transformer decoder from scratch with no starter code in about 30 minutes, including embeddings, masked multi-head self-attention, feed-forward layers, residual connections and layer normalization. Tests tensor-shape fluency, causal masking and hands-on deep learning coding for a machine learning engineer interview.

  Machine LearningTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Maximize total catch over K hours from ponds whose yield drops after each hour

  Given the current hourly catch of each fishing pond and a budget of K hours, where every hour fished at a pond lowers its catch by one, compute the maximum total number of fish. It tests modeling the declining yields, choosing an optimal schedule, and meeting a time bound better than O(K log N) when K is very large.

  Coding & AlgorithmsTechnical Screen
• BByteDanceHard·Machine Learning Engineer
  Minimum Number of Rooms to Host a Set of Possibly Overlapping Meetings

  Given meetings as half-open start and end time intervals, compute the minimum number of rooms needed so that no room ever hosts two overlapping meetings. Tests interval reasoning, correct handling of back-to-back meetings that share an endpoint, and an efficient approach for up to 100,000 meetings.

  Coding & AlgorithmsTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Reverse a Linked List in Groups of K

  This problem tests your ability to manipulate pointers in a linked list structure and manage iterative or recursive state transitions for fixed-size segments.

  Coding & AlgorithmsTechnical ScreenLinked ListPointersAlgorithms
• CCadenceMedium·Machine Learning Engineer
  Explain Transformer Positional Encoding

  This question tests your conceptual and technical understanding of why Transformers require positional encodings and the mathematical mechanics of different encoding strategies.

  Machine LearningTechnical ScreenTransformerAttentionNLP
• CCadenceMedium·Machine Learning Engineer
  Implement Depth-First Search Traversal

  This problem tests your ability to implement a recursive or iterative depth-first search (DFS) while managing state to handle cycles in a graph represented as an adjacency list.

  Coding & AlgorithmsTechnical ScreenGraphsDFSRecursion
• CitadelHard·Machine Learning Engineer
  Make a hard MoE router differentiable

  Make a hard MoE router differentiable 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
• CCognitivMedium·Machine Learning Engineer
  Design a Real-Time Feature Store

  This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.

  ML System DesignTechnical Screen
• CCognitivMedium·Machine Learning Engineer
  Maintain a Streaming Median

  This question evaluates understanding of streaming algorithms and dynamic data structures for maintaining order statistics, along with analysis of time complexity and memory trade-offs.

  Coding & AlgorithmsTechnical Screen
• CCreditkarmaMedium·Machine Learning Engineer
  Design Personalized Promotion Recommendations

  This question tests the ability to design an end-to-end recommendation system for personalized financial promotions, focusing on handling sparse conversion data, real-time feature requirements, and feedback loops.

  ML System DesignTechnical ScreenRecommendation SystemsRankingFeature Engineering
• DatabricksMedium·Machine Learning Engineer
  Design In-Memory QPS Counter

  This question evaluates the ability to design and implement an in-memory time-series counter and sliding-window aggregation, emphasizing efficient data structures, handling high-frequency events, and memory-time trade-offs.

  Coding & AlgorithmsTechnical Screen
• DatadogHard·Machine Learning Engineer
  Implement Focal Loss for Class-Imbalanced Classification

  Tests the ability to implement binary focal loss with raw logits from first principles, emphasizing numerical stability, custom weighting, and tensor reduction strategies.

  Machine LearningTechnical Screenmachine-learningdeep-learningpytorch
• DatadogHard·Machine Learning Engineer
  Implement Grouped-Query Attention (GQA)

  Implement the Grouped-Query Attention (GQA) mechanism commonly used in modern LLMs to balance serving memory bandwidth with generation quality. You will handle tensor reshaping/expansion across query head groups, scaled dot-product attention, and causal masking.

  Machine LearningTechnical Screendeep-learningtransformerspytorch
• DDeshawMedium·Machine Learning Engineer
  Implement sqrt and parity-based array rearrangement

  In the Coding & Algorithms domain, this prompt evaluates numerical methods and precision handling for implementing a square-root function without library support, together with in-place array manipulation, partitioning and complexity reasoning for parity-based rearrangement, testing competencies in algorithm design, numerical approximation, and space-efficient data transformations. These problems are commonly asked to assess understanding of numerical stability, error bounds and iterative approximation as well as mastery of in-place ordering/partitioning strategies and time/space complexity analysis, requiring both conceptual understanding of mathematical properties and practical implementation ability.

  Coding & AlgorithmsTechnical Screen