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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 3991–4020 of 4616 results
- TeslaMediumSoftware EngineerDescribe how you use Kubernetes
Describe how you use Kubernetes 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 - 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 - TeslaEasySoftware EngineerDesign a JSON Merge API with Pluggable Behaviors
Implement a non-mutating JSON merge utility with recursive object handling, configurable array merge strategies, and an extensible design for custom merge policies.
Software Engineering FundamentalsTechnical Screenjsonrecursionapi-design - TeslaHardSoftware EngineerDesign a Supercharger Backend: Billing, Stall Telemetry and Nearest Available Charger
System design question on the backend for an electric-vehicle fast-charging network: billing each session by the energy delivered, storing stall health telemetry long term, and showing drivers the nearest available charger with its price. Probes secure device communication, DNS and routing, local buffering and sync versus async choices.
System DesignTechnical Screen - TeslaMediumSoftware EngineerDesign a contiguous segment allocator
This question evaluates a candidate's competency in designing and implementing efficient data structures for interval management, contiguous memory allocation, fragmentation handling, tag-based resource tracking, and algorithmic complexity analysis.
Coding & AlgorithmsTechnical Screen - TeslaEasyBusiness Intelligence EngineerDesign an End-to-End Customer Delivery Experience Dashboard
Design an end-to-end delivery experience dashboard connecting executive KPIs to stage-level operational diagnostics, with clear metric definitions, data freshness SLAs, and handling of late-arriving events.
Analytics & ExperimentationTechnical Screenbusiness-intelligencedashboard-designmetric-definition - TeslaHardSoftware EngineerDesign concurrency-safe shared payment account API
Design concurrency-safe shared payment account API 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 - 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 - TeslaMediumSoftware EngineerFind latency pairs with minimal difference
This question evaluates array manipulation, pairwise comparison, and algorithmic efficiency by requiring identification of minimal absolute differences and construction of ordered latency pairs.
Coding & AlgorithmsTechnical Screen - TeslaEasySoftware EngineerHandle API Errors with Python Requests
Tests your ability to write resilient HTTP client logic in Python using requests, distinguishing between retryable and non-retryable errors, handling rate limits, and safely parsing JSON.
Software Engineering FundamentalsTechnical Screenpythonnetworkingapi - 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 - TeslaHardSoftware EngineerImplement Reduce and Derive Map and Filter from It
Explain reduce semantics, derive ordered map and filter with a reducer, and compose them over table rows while handling empty input and accumulator costs.
Software Engineering FundamentalsTechnical Screen - TeslaHardSoftware EngineerImplement a Rollback Key-Value Store
This problem tests your ability to design a data structure that supports basic key-value operations with an added transactional rollback mechanism using a stack-based approach.
Coding & AlgorithmsTechnical ScreenData StructuresStackVersioning - 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 - TeslaMediumSoftware EngineerOptimize Trapping Rain Water
This question evaluates algorithmic problem-solving with arrays and analysis of time-space trade-offs in the Coding & Algorithms domain, and is commonly asked to assess a candidate's ability to transform brute-force approaches into efficient linear-time solutions while reasoning about memory usage.
Coding & AlgorithmsTechnical Screen - TeslaMediumData EngineerWrite SQL Data Transformation Queries
This question tests the ability to handle window functions for running totals and conditional aggregation to transform raw manufacturing logs into formatted monthly production reports.
Coding & AlgorithmsTechnical ScreenSQLWindow FunctionsAggregation - The New York TimesEasyData AnalystWrite SQL for content-view analytics
This question evaluates proficiency in SQL-based data manipulation and analytics, including aggregation, distinct counting, NULL handling, timestamp/hour extraction, device grouping, and row-level ordering, and is situated in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - The Trade DeskEasySoftware EngineerAdd a click listener to a button
Add a click listener to a button evaluates requirements, assumptions, structured reasoning, trade-offs, and verification in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsTechnical Screen - The Trade DeskEasyData ScientistAnswer common DS behavioral screen questions
This question evaluates communication, decision-making, project execution, technical depth in modeling and evaluation, learning agility, and stakeholder management for a Data Scientist internship within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - The Trade DeskEasyData ScientistDesign a CVR model for RTB bidding
This Trade Desk data-science screen asks you to design an end-to-end conversion-rate (CVR) model for real-time bidding (RTB) at a DSP. It covers RTB system roles, label/attribution-window definition, bid-time feature engineering and leakage, logistic-regression vs LightGBM tradeoffs, log-loss training, class-imbalance handling with calibration, PR-AUC vs ROC-AUC evaluation, and latency-aware, safely-deployed production.
Machine LearningTechnical Screen - The Trade DeskEasyData ScientistImplement monotonic-array linear interpolation
This question evaluates proficiency with piecewise-linear interpolation on strictly increasing (monotonic) arrays, sound handling of out-of-range queries (error, clamp, extrapolate, or None/NaN), and efficient interval search over sorted data with a target of O(log n). It also probes real-world judgment about when each out-of-range policy is appropriate and which non-bisect strategies exist.
Coding & AlgorithmsTechnical Screen - ThumbtackMediumData ScientistEstimate Two Conditional Win Probabilities by Simulation
This question tests practical coding ability in probabilistic simulation, requiring implementation of a Monte Carlo method to estimate conditional win probabilities under two decision strategies. It assesses a data scientist's skill in translating a classic probability scenario into working randomized code rather than relying on closed-form analysis.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistAnalyze DFS, BFS, and A* trade-offs
This question evaluates understanding of graph search algorithms, heuristic properties (admissibility and consistency), path optimality and time/space complexity, requiring application of BFS, uniform-cost search, A*, and DFS on a concrete weighted graph.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistAnalyze Negative Reviews' Impact on Coupon Repurchase Rate
Evaluates causal analysis of negative reviews on coupon repurchase behavior using exposure-aware data. Strong answers define treatment and outcome, collect exposure data, control confounding, and validate estimates with robustness checks.
Analytics & ExperimentationTechnical Screen - TikTokMediumSoftware EngineerAnswer common behavioral questions using STAR
This question assesses the candidate's ability to communicate past professional experiences using the STAR method, focusing on accountability, problem-solving, and collaboration in a high-pressure environment.
Behavioral & LeadershipTechnical ScreenCommunicationSTAR MethodConflict Resolution - TikTokMediumSoftware EngineerAnswer core Python, CS, DB, and testing questions
This problem tests foundational software engineering knowledge across Python internals, database indexing, and testing strategy through a multi-part conceptual assessment.
Software Engineering FundamentalsTechnical ScreenPythonDatabasesSystems Design - TikTokMediumData ScientistCalculate Conversion Rate from Ad Clicks to Page Visits
This question evaluates a candidate's competency in event-level data manipulation and metric computation using SQL or Python, including time-based aggregation, deduplication, and joining or windowing of event streams.
Data Manipulation (SQL/Python)Technical Screen
Tesla
The Trade Desk
Thumbtack
TikTok