xAI interview questions

Showing 31–53 of 53 results

• Greedy Longest-Match Tokenizer for an LLM Data PipelineHard

  This question evaluates understanding of string-processing and greedy maximal-munch tokenization, as well as competency in designing efficient prefix-matching data structures and handling large-scale text preprocessing for LLM pipelines.

  xAIMachine Learning EngineerCoding & AlgorithmsOnsite
• How Would You Prevent Hallucinations in an LLM-Based System?Medium

  This question evaluates a candidate's understanding of hallucination in large language models, including underlying causes, detection, and system-level mitigation approaches, as well as their ability to reason about model reliability and failure modes.

  xAISoftware EngineerMachine LearningHR Screen
• Implement Distributed Matrix MultiplicationHard

  This problem tests your ability to design a distributed matrix multiplication algorithm by partitioning large matrices across multiple worker nodes to minimize communication overhead.

  xAIMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement a Radix Cache for Integer SequencesMedium

  This question evaluates understanding of radix trees (prefix-compressed tries), sequence-oriented trie manipulation, and the maintenance of invariants like distinct branching and maximal compression, and belongs to the Coding & Algorithms domain.

  xAISoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a parallelized sortMedium

  This problem tests your ability to implement a parallelized version of Merge Sort using threads or a thread pool, focusing on partitioning, synchronization, and managing recursion depth.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Implement a trie-based tokenizerHard

  This question evaluates a candidate's competency in designing and implementing a production-grade subword tokenizer, covering trie-based longest-prefix matching, Unicode-aware text processing, normalization and casing impacts, whitespace/punctuation rules, fallback strategies, performance and memory trade-offs, versioning, and testing for edge cases. It is commonly asked in ML System Design interviews to assess practical implementation skills and conceptual trade-off reasoning for deterministic, scalable LLM preprocessing, and tests both practical application and conceptual understanding within the ML System Design domain.

  xAIMachine Learning EngineerML System DesignTechnical Screen
• Implement an Asynchronous React TypeaheadMedium

  Implement a debounced React typeahead with stale-response protection, cancellation, loading/error states, keyboard selection, and accessible combobox semantics.

  xAIFrontend EngineerSoftware Engineering FundamentalsOnsite
• Implement dynamic batching for token decodingMedium

  This question evaluates dynamic batching, per-request state management, and sequence-decoding correctness for language-model inference, including handling stop conditions, max-token limits, and maintaining a correct slot-to-request mapping.

  xAIMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement two string utility functionsMedium

  This question evaluates proficiency in string manipulation and text processing, including character counting, tokenization by spaces, case-sensitive comparisons, and preserving original word order.

  xAIData EngineerCoding & AlgorithmsTechnical Screen
• In-Memory Key-Value Database with Nested TransactionsEasy

  This question evaluates understanding of in-memory data structures, transactional semantics (including nested transactions), and algorithmic complexity for implementing a key-value store that supports efficient GET/SET and rollback/commit operations.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Load and Display a React List with useEffectMedium

  Load a React list with useEffect while handling endpoint changes, cleanup, stale responses, HTTP and schema errors, stable keys, and user-visible states.

  xAIFrontend EngineerSoftware Engineering FundamentalsOnsite
• Make a Racy BankAccount Deposit Thread-Safe Without Editing the Class, in Python and GoMedium

  A concurrency question built around a BankAccount class whose deposit method reads the balance, sleeps, then writes it back. Candidates find every race, make deposits thread-safe through a subclass or wrapper without editing or copying the class, and carry the fix over to Go, which has no inheritance.

  xAISoftware EngineerSoftware Engineering FundamentalsOnsite
• Maximize distinct values after unique ± offsetsHard

  This problem tests greedy strategy and sorting to determine the maximum possible count of distinct values in an array after applying exactly one offset from a given set of allowed integers to each element.

  xAISoftware EngineerCoding & AlgorithmsTechnical Screen
• Minimum Time to Run All Jobs with a Cooldown Between Identical JobsMedium

  This question evaluates understanding of scheduling constraints, frequency analysis, and time-complexity reasoning required to minimize total execution time under per-type cooldown restrictions.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Phone Keypad Predictive Text SuggestionsEasy

  This question evaluates understanding of string-to-digit encoding, prefix-based retrieval, and efficient preprocessing for fast query-time lookup in large dictionaries.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Plan a Project Across Customers and Partner TeamsMedium

  Plan a cross-team customer project through outcome clarification, dependency ownership, milestone evidence, uncertainty, scope tradeoffs, and clear stakeholder communication.

  xAIFrontend EngineerProduct / Decision MakingOnsite
• Render Nested JSON in a Code-Style ViewerMedium

  Render nested JSON safely in React with indentation, type styling, collapsible containers, empty values, and explicit boundaries between a viewer and an editor.

  xAIFrontend EngineerSoftware Engineering FundamentalsOnsite
• Sort a Large Array Using Multiple ThreadsMedium

  This question evaluates understanding of concurrent programming, parallel algorithm design, sorting algorithms, and merging techniques under multi-threaded execution, focusing on thread-safety, deterministic correctness despite scheduling variability, and performance trade-offs.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Three Exceptional Achievements, a Recent-Projects Pitch, and a Project Deep DiveMedium

  A behavioral preparation set built around project storytelling: write three one-to-two-sentence exceptional achievements, give a five-minute overview of recent projects, and present a 30-minute project deep dive followed by detailed technical questioning. It tests concise impact, ownership and depth.

  xAISoftware EngineerBehavioral & LeadershipOnsite
• Versioned Key-Value Store with Nested Transactions (Begin, Commit, Rollback)Medium

  This question evaluates understanding of transactional state management, nested transactions, visibility semantics for set/delete operations, and efficient in-memory data-structure manipulation.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• Walk Through a Past Frontend Project: Problems Solved and Tech Stack ChoicesMedium

  A behavioral question from a project and product management round for a frontend role: describe a problem you hit on a past project and how you solved it, then explain how you chose the tech stack. It tests structured storytelling, trade-off reasoning, and decisions tied to product needs.

  xAIFrontend EngineerBehavioral & LeadershipOnsite
• Weighted LRU Cache: Capacity Counted by Item QuantityMedium

  This question evaluates understanding of cache design and LRU eviction semantics with weighted resource accounting, plus data-structure and amortized-complexity reasoning needed to support O(1) average-time operations.

  xAISoftware EngineerCoding & AlgorithmsOnsite
• What are processes vs threads?Medium

  This question evaluates understanding of concurrency concepts—processes versus threads—including memory/address-space management, shared-memory versus inter-process communication, performance trade-offs for CPU-bound versus I/O-bound workloads, and failure/isolation implications such as crash containment.

  xAIMachine Learning EngineerSoftware Engineering FundamentalsOnline Assessment