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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 301–330 of 381 results
- TeslaEasyMachine Learning EngineerImplement 2D convolution forward pass
This question evaluates understanding of 2D convolution mechanics and practical low-level tensor manipulation, including kernel shape handling, padding and stride effects, multi-channel input/output interactions, and optional bias integration.
Coding & AlgorithmsTechnical Screen - TeslaMediumMachine Learning EngineerImplement and vectorize NumPy Conv2D
This question evaluates understanding of 2D convolution mechanics, multidimensional NumPy array manipulation, and the competency to optimize numerical computations for performance and memory.
Data Manipulation (SQL/Python)Technical Screen - TeslaHardMachine Learning EngineerImplement attention and Transformer with backward pass
This question evaluates implementation and analytical skills for scaled dot-product multi-head self-attention and an encoder-style Transformer block, including manual forward and backward-pass computations, gradient derivation for projection matrices, causal masking for autoregressive next-token prediction, numerical stability of softmax, and time/memory complexity analysis within the Machine Learning domain, emphasizing practical implementation with underlying conceptual understanding of linear algebra and backpropagation mechanics. It is commonly asked to verify mastery of attention mechanisms and autodiff-free gradient reasoning, plus the ability to compute cross-entropy/perplexity, perform finite-difference gradient checks, and reason about algorithmic trade-offs.
Machine LearningTechnical Screen - TeslaMediumMachine Learning EngineerImplement automatic braking logic in Python
This question evaluates understanding of kinematics, motion modeling, and numerical reasoning for safety-critical control logic, focusing on stopping-distance computation and the impact of reaction time on braking decisions.
Coding & AlgorithmsTechnical Screen - TeslaHardMachine Learning EngineerModel other agents in simulation
This question evaluates a candidate's competency in modeling other agents within simulation environments, focusing on designing agent behavior models and assessing their impact on training and evaluation robustness.
ML System DesignTechnical Screen - TikTokMediumMachine Learning EngineerCompute city skyline silhouette
Compute city skyline silhouette 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 - TikTokMediumMachine Learning EngineerCompute minimum path sum in a triangle
This problem tests your ability to optimize path calculations in a non-linear data structure using dynamic programming, specifically focusing on space complexity optimization.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingArraysSpace Optimization - TikTokMediumMachine Learning EngineerCount subarrays summing to target
This problem tests your ability to optimize subarray sum calculations from an O(n^2) approach to O(n) using a hash map for prefix sum tracking.
Coding & AlgorithmsTechnical ScreenArraysHash MapPrefix Sum - TikTokHardMachine Learning EngineerDefine QKV for recommender cross-attention
This question evaluates understanding of Transformer-style cross-attention and the concrete design of Query, Key, and Value tensors for deep-learning recommender systems, testing representation semantics, embedding alignment, and interaction modeling between user history, candidate items, and context.
Machine LearningTechnical Screen - TikTokMediumMachine Learning EngineerDescribe internship and research projects
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe internship and research projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokHardMachine Learning EngineerDesign query generation to maximize CTR
Design query generation to maximize CTR 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 - TikTokMediumMachine Learning EngineerDesign video captioning under compute limits
This question evaluates expertise in ML system design for multimodal large models, covering deployment under compute and GPU memory constraints as well as large-scale retrieval and processing of video captions and embeddings.
ML System DesignTechnical Screen - TikTokMediumMachine Learning EngineerDetermine if a string can be segmented
This question evaluates a candidate's understanding of dynamic programming and string-processing skills, along with algorithmic complexity analysis for segmentation problems.
Coding & AlgorithmsTechnical Screen - TikTokMediumMachine Learning EngineerDifferentiate virtual and pure virtual functions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Differentiate virtual and pure virtual functions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - TikTokMediumMachine Learning EngineerExplain FlashAttention, KV cache, and RoPE
This problem tests your understanding of core transformer efficiency techniques, specifically focusing on how they improve memory footprint, latency, and sequence length handling.
Machine LearningTechnical ScreenTransformersAttentionInference Optimization - TikTokMediumMachine Learning EngineerExplain Transformer, GPT vs BERT, and PR metrics
This question evaluates understanding of modern NLP model architecture and evaluation metrics, specifically Transformer block components, key distinctions between GPT and BERT including pretraining objectives and usage, and precision/recall interpretation, testing competencies in deep learning architecture knowledge, model selection reasoning, and metric-based evaluation within the Software Engineering Fundamentals domain for a Machine Learning Engineer role. It is commonly asked because it probes both conceptual understanding of architectures and pretraining paradigms and practical application of performance trade-offs—assessing conceptual understanding alongside practical application in threshold-based precision versus recall decisions.
Software Engineering FundamentalsTechnical Screen - TikTokMediumMachine Learning EngineerExplain your VLM project end-to-end
This question evaluates proficiency in vision-language model engineering, including model architecture (vision encoder, language model, fusion), data curation and distribution, training recipes, evaluation metrics, inference latency, and limitations analysis within the Machine Learning domain of multimodal/Vision-Language models.
Machine LearningTechnical Screen - TikTokMediumMachine Learning EngineerFind and count target-sum subarrays
Find and count target-sum subarrays 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 - TikTokMediumMachine Learning EngineerFind length of longest common subsequence
This problem tests your ability to identify overlapping subproblems and optimal substructure by implementing a dynamic programming solution to find the length of the Longest Common Subsequence (LCS) between two strings.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingStringsOptimization - TikTokMediumMachine Learning EngineerFix a BST with two misplaced keys
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Fix a BST with two misplaced keys states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - TikTokHardMachine Learning EngineerGenerate all safe queen placements on board
This question evaluates algorithm design for constraint-satisfaction problems, testing skills in search, recursion, backtracking, combinatorics, and state-space pruning.
Coding & AlgorithmsTechnical Screen - TowerMediumMachine Learning EngineerExplain the KV Cache and Prefill-Decode Disaggregation in LLM Serving
Explain how the KV cache speeds up autoregressive decoding in transformer language models, estimate how much GPU memory it needs, and describe prefill-decode disaggregation in LLM serving. It tests understanding of compute-bound versus memory-bound inference phases, cache sizing and the trade-offs of splitting the phases across GPU pools.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerExplain ML basics and recommender tuning
This question evaluates core machine learning competencies including overfitting and regularization, ensemble methods like bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), hyperparameter tuning in production, and end-to-end recommender system design and deployment.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerImplement K-Means and compare with GMM
This question evaluates understanding and practical implementation of clustering algorithms, specifically K-Means centroid initialization, assignment and update mechanics, convergence criteria and edge-case handling, along with comparative knowledge of Gaussian Mixture Models and the Expectation-Maximization training framework.
Coding & AlgorithmsTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)
This question tests ML system design competency, specifically the ability to architect end-to-end recommendation pipelines covering retrieval, ranking, feature engineering, and low-latency serving at scale. It assesses practical knowledge of two-stage recommender systems, implicit feedback modeling, cold-start handling, and critical evaluation of AI-generated code — skills central to machine learning engineering interviews.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Thumbnail Selection for a Streaming Catalog
This ML system design question tests a candidate's ability to architect an end-to-end personalization and ranking system, covering problem framing, reward modeling, and low-latency serving at scale. It evaluates practical knowledge of contextual bandits, engagement-signal design, cold-start handling, and off-policy evaluation — core competencies for machine learning engineering roles.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerMachine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings
This question assesses foundational machine learning knowledge across multiple core domains, including tree-based models, supervised training, model evaluation, embeddings, and transformer architecture. It is commonly used in ML engineer interviews to gauge whether a candidate can explain key concepts with correct mechanics, articulate trade-offs, and reason through failure modes.
Machine LearningTechnical Screen - TubiMediumMachine Learning EngineerWeighted Random Sampling by Index
This question tests a candidate's ability to implement weighted random sampling using prefix sums and binary search, evaluating practical algorithm design for probability distributions. It assesses proficiency in translating mathematical probability constraints into efficient data structures, a skill commonly probed in machine learning engineering roles where non-uniform sampling is fundamental to model training and inference pipelines.
Coding & AlgorithmsTechnical Screen - UberEasyMachine Learning EngineerAnalyze sales with groupby
This question evaluates proficiency in data manipulation and analytics using pandas, specifically the ability to join relational tables and apply groupby aggregations to compute business metrics like total sales, average sales per order, and completion rates.
Coding & AlgorithmsTechnical Screen - UberMediumMachine Learning EngineerCompute currency conversion via graph search
This question evaluates graph modeling and pathfinding with weighted edges, numerical reasoning about multiplicative rates and floating-point precision, and the ability to analyze algorithmic time and space complexity.
Coding & AlgorithmsTechnical Screen
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