practice a little every day, paws crossed ♡
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 8281–8310 of 9403 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 - TeslaHardSoftware EngineerDesign a Distributed Blob Storage System for Build Artifacts
Design a distributed storage system for build artifacts, similar to a distributed NoSQL database but storing opaque blobs with looser ACID requirements. It tests separating immutable data from consistent metadata, upload atomicity, replication and repair, handling download fan-out, and safe retention and garbage collection.
System DesignOnsite - 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 Video Sharing Platform
Design a resilient, large-scale video sharing and streaming platform that handles multi-gigabyte uploads, asynchronous transcoding, and low-latency global delivery. This question tests your ability to design chunked upload protocols, idempotent processing pipelines, and CDN-backed adaptive streaming architectures.
System DesignOnsiteDistributed SystemsVideo StreamingCDN - 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 - 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 - TeslaMediumSoftware EngineerDesign an Order Fulfillment System
Design an order-fulfillment system and explain its major components, data flow, and reliability trade-offs.
System DesignOnsite - TeslaMediumBackend EngineerDesign booking and settlement systems
This question evaluates backend system design competencies such as concurrency control, transactional integrity, reconciliation, file ingestion and processing, idempotency, API design, and operational reliability.
System DesignOnsite - 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 - TeslaMediumSoftware EngineerDesign synchronization for water molecule assembly
This question evaluates concurrency and synchronization skills, including thread coordination, use of locks/semaphores/condition variables, and reasoning about safety and liveness properties in concurrent systems.
System DesignHR Screen - TeslaMediumSoftware EngineerDiscuss Distributed-System Experience with Go, Java, Spark, and Elasticsearch
Tests your ability to clearly explain distributed systems architecture, justify technology selections (such as Go, Java, Spark, and Elasticsearch), and discuss operational bottlenecks and fault tolerance from real production experience.
Software Engineering FundamentalsHR ScreenDistributed SystemsGoJava - 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 - TeslaMediumBackend EngineerExplain reliability and runtime fundamentals
This question evaluates understanding of reliability and runtime fundamentals in backend engineering, covering competencies such as fault tolerance, end-to-end request lifecycles, API gateway degradation, storage engine selection for search, and the distinction between compilers and interpreters.
Software Engineering FundamentalsOnsite - 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 - TeslaHardSoftware EngineerFlatten a Linked List with Nested Side Branches into One Doubly Linked List
A linked list whose nodes may each carry one side branch, with branches nested like a binary tree, must be flattened into a single doubly linked list with every branch spliced between its parent and the parent's original successor. It tests pointer rewiring, ordering rules for nested branches, and handling very deep nesting.
Coding & AlgorithmsOnsite - TeslaHardSoftware EngineerFrom a Monorepo Commit to an Over-the-Air Vehicle Update, and Choosing What to Build
Trace everything that happens between an engineer pushing a commit to a monorepo and the resulting software being installed on customers' cars, then explain how the monorepo decides which targets to build and test and in what order. It tests CI and release gating, artifact provenance, staged over-the-air rollout, safe in-vehicle installation, and dependency-graph builds.
System DesignOnsite - TeslaEasySoftware EngineerGenerate Per-Position Guess Feedback
This problem tests your ability to process strings and manage state (character frequency) to determine exact matches versus partial matches, commonly used in word-guessing games.
Coding & AlgorithmsOnline AssessmentStringHash MapArray - 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 - TeslaMediumSoftware EngineerHow to Identify Best Battery Group
This question evaluates understanding of statistical inference, experimental design, and metric construction for binary test outcomes, including defining composite quality measures, handling unequal sample sizes, quantifying uncertainty, and addressing multiple-comparison issues.
Machine LearningOnsite - 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 - TeslaMediumSoftware EngineerInvestigate Recurring Bugs and Prevent the Same Failure from Returning
Implement an invariant analysis engine to identify recurring bugs across production incident traces, pin down the earliest violating step, and flag failures recurring above a frequency threshold.
Software Engineering FundamentalsHR ScreenDebuggingTestingData Processing - 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
Tesla