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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.
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Showing 661–690 of 782 results
- Startups.ComMediumMachine Learning EngineerImplement common neural network layers in PyTorch
This question evaluates proficiency in implementing core neural-network primitives (ReLU, numerically-stable Softmax, LayerNorm, BatchNorm with running statistics, and RMSNorm), numerical stability and normalization concepts, and understanding of autograd-compatible tensor operations.
Coding & AlgorithmsOnsite - StripeMediumMachine Learning EngineerDesign payment fraud detection
This question evaluates a candidate's ability to design end-to-end machine learning systems for payment fraud detection, covering competencies in labeling, imbalanced learning, feature engineering across users/merchants/cards/devices/IPs and graph relationships, model selection, online serving, latency and feature freshness, monitoring, experimentation, and adversarial robustness. As an ML System Design problem for a Machine Learning Engineer role, it is commonly asked to probe trade-offs between fraud loss, false positives, user friction and latency and requires both conceptual understanding and practical application of production ML concerns such as evaluation metrics, thresholding, retraining, and operational constraints.
ML System DesignTechnical Screen - StripeMediumMachine Learning EngineerNormalize targets for multitask regression
This question evaluates proficiency in multitask regression, preprocessing, and loss engineering within the Machine Learning domain, focusing on handling heterogeneous target distributions, sign-constrained labels, normalization, balanced loss combination, and mapping predictions back to original units at inference.
Machine LearningTechnical Screen - TeslaHardMachine Learning EngineerCompare RNNs, LSTMs, Transformers, and MPC
This question evaluates understanding of sequence-modeling architectures (RNNs, LSTMs, Transformers) and Model Predictive Control, assessing architectural choice, training and optimization trade-offs, handling of long-range dependencies, and methods for integrating learned dynamics with control.
Machine LearningTechnical Screen - TeslaEasyMachine Learning EngineerCompute Conv2D parameter counts
This question evaluates understanding of convolutional neural network parameterization, specifically how kernel dimensions, input/output channels and an optional bias term determine the number of learnable parameters.
Machine LearningTechnical Screen - TeslaMediumMachine Learning EngineerCompute nearest index within threshold after walking distances
This question evaluates a candidate's proficiency in data manipulation, numerical computing with NumPy, spatial reasoning for Euclidean distance and interpolation along polylines, and designing time- and space-efficient algorithms for large-scale inputs.
Data Manipulation (SQL/Python)Technical Screen - TeslaHardMachine Learning EngineerCompute suffix sums over waypoints
This question evaluates a candidate's ability to compute geometric suffix sums over batched 2D trajectories, testing skills in array manipulation, Euclidean distance computation, and runtime-conscious algorithm design within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - TeslaHardMachine Learning EngineerDesign RL reward for speed limits
This question evaluates reinforcement learning competencies—reward engineering for speed-constrained control, distinctions in policy optimization methods (PPO versus GRPO), and reasoning about time-varying constraints and partial observability using trajectory data.
Machine LearningTechnical Screen - TeslaHardMachine Learning EngineerDesign an LLM math-solving chain
Design an LLM math-solving chain 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 DesignOnsite - TeslaHardMachine Learning EngineerExplain and derive importance sampling estimators
This question evaluates understanding of importance sampling, Monte Carlo estimators, weight normalization, variance behavior, optimal proposal selection, and effective sample size, assessing competency in statistical estimation and Monte Carlo methods.
Statistics & MathTechnical Screen - 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 - Thomson ReutersEasyMachine Learning EngineerApplied Machine Learning Assessment: Metrics, Evaluation, and NLP Fundamentals
Practice a Thomson Reuters machine learning interview question about applied machine learning assessment: metrics, evaluation, and nlp fundamentals. The prompt covers objective framing, data assumptions, evaluation, trade-offs, and production risks without giving away the answer.
Machine LearningOnline Assessment - TikTokHardMachine Learning EngineerAnswer ML fundamentals and diagnostics questions
This problem tests your theoretical understanding of model diagnostics, ensemble learning mechanics, and the trade-offs between optimization strategies in machine learning.
Machine LearningOnline AssessmentEnsemble MethodsRegularizationMetrics - 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 - TikTokMediumMachine Learning EngineerDesign a model to choose dynamic K
This question evaluates a candidate's competency in ML system design and decision-focused modeling for recommender pipelines, focusing on retrieval-ranking trade-offs, latency and compute cost constraints, and dynamic per-request selection of K.
ML System DesignOnsite - 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 frog reachability across stones
This question evaluates dynamic programming, state-space reachability, and constrained transition reasoning, along with skills in hash-based memoization and time/space complexity analysis.
Coding & AlgorithmsOnsite - 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
Stripe
Tesla
TikTok