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xAI interview notes, page 4 of 9

Mid-sized

Most of these xAI write-ups come from Software Engineers (74), then Machine Learning Engineers (11) and Backend Engineers (1). The most reported rounds are the technical phone screen (52), the system design round (12), the behavioral / leadership round (6) and the onsite coding round (4).

86 interview notes · updated Jul 2026

xAI·Software Engineer·Technical Phone Screen

May 2026
xAI software engineer interview with a classic LRU cache design problem. The constraint about not using built-in ordered structures was the real test, pretty standard for this level but the O(1) requirement forces you to actually think.
  • Design and implement a fixed-capacity LRU cache from scratch supporting get and put in O(1) time, without using any built-in ordered map or cache utility.

“The O(1) constraint is what makes this non-trivial.”

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xAI·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at xAI for a software engineer role. The whole session was basically one big question about rate limiting, but it had enough moving parts that it kept branching into follow-ups for a while.
  • Design a distributed rate limiter for a high-traffic API platform where every user can have a different token quota. Walk through the algorithm, how per-user quotas are stored and accessed, the data model for live counters, the request-time decision flow, and how the system scales.
  • How would you enforce a single user's quota globally across multiple regions without adding cross-region latency to every request?
  • How would you support burst allowances for a user, like someone sustained at 100 tokens per second who can briefly spike to 1,000, without changing the underlying algorithm?
  • A user gets upgraded mid-flight from 100 to 10,000 tokens per second. Trace exactly how and when the new quota takes effect in your design, and what happens to their existing bucket state.
  • How would you rate-limit across multiple dimensions simultaneously, like per user, per IP, and per endpoint, and in what order would you evaluate them?

“This is the kind of question that sounds scoped until you realize how many sub-problems are hiding inside it.”

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xAI·Software Engineer·Recruiter / HR Screen

May 2026
Recruiter screen at xAI that was basically just a structured 'sell yourself' prompt. One question, five to seven minutes, and you better have a clean throughline ready.
  • Walk me through your background and what you've worked on, with a focus on scope of impact, technical depth, and how it connects to this role.

“Pretty standard opener but I always underestimate how much structure it actually needs.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Short intro screen at xAI for a software engineer role, maybe 15 minutes tops. Three questions, all centered on your current project. Pretty low-pressure but you need to know your work cold.
  • Tell me about yourself and what you're currently working on.
  • Walk me through your current project end-to-end, including the technical details.
  • What were the main technical challenges on that project and how did you handle them?

“Standard opener but I always fumble the balance between too much background and too little.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Xai software engineer interview that was basically a full backend implementation exercise. They had me design a checkers game from scratch, which sounds simple enough until you're actually in it trying to remember how capture chains work under pressure.
  • Design and implement the backend for a checkers game, covering board setup, move validation, capture logic (jumping over opponent pieces), and win condition detection.

“I started with board initialization since that felt safe, a 2D grid with piece positions.”

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xAI·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at xAI for a software engineer role. The whole session was basically one big deep-dive into distributed key-value store design, and they went pretty far into the weeds on trade-offs.
  • Design a distributed key-value store. Walk through the API, consistency model, replication, partitioning, and persistence layer.
  • How would you partition data across nodes, and what happens when you add or remove a node from the cluster?
  • Compare LSM-tree and B-tree storage engines for this use case. Which would you pick and why?
  • How do you handle hot keys in a distributed key-value store?
  • What is your approach to failure detection, and how do you reconcile diverged replicas?

“This was the whole interview, not just a warmup.”

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xAI·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Research-flavored behavioral round at xAI for a software engineering role. Two questions, both with a clear slant toward ML/RL work, and the interviewer was probing pretty hard to connect your past experience to what the team actually does.
  • What is the most technically challenging project you have worked on? Walk through the problem, your approach, the key technical hurdles, and the outcome.
  • How do you decide which reinforcement learning environments to use for training and evaluation? Talk through how you think about task diversity, difficulty calibration, reward shaping, avoiding data leakage, and how you choose what to keep, drop, or weight.

“Sounds like a standard opener but it really wasn't.”

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xAI·Software Engineer·Take-home Assignment

May 2026
Take-home for a backend role at xAI. The assignment was meatier than I expected, basically a mini system design project you had to actually ship as working code with tests.
  • Implement two rate limiters working in tandem: one capping inbound API traffic at 100 requests per second, and a second limiting a downstream consumer to 10 requests per second. Design the request flow including queuing and back-pressure handling, pick a rate limiting algorithm and justify it, write working code with tests, and document your trade-offs.

“The two-limiter part is what trips you up.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Got a coding question at xAI that looked straightforward until I realized the naive approach was going to get me dinged on performance. The problem was more interesting than I expected and I think I fumbled the optimal solution explanation even after I had the right idea.
  • Given an integer array and a non-negative integer k, you can shift each element by any amount in [-k, k] independently. What is the maximum number of distinct values you can produce in the resulting array?

“My first instinct was to just iterate over every possible value in each element's window and greedily assign, which is O(N*K) and they were not impressed.”

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xAI·Machine Learning Engineer·Technical Phone Screen

May 2026
Deep technical screen for an ML Engineer role at xAI. The whole session was basically one long design problem around building a tokenizer from scratch, and it went pretty far into the weeds on Unicode, complexity analysis, and production trade-offs.
  • Design a subword tokenizer for an LLM pretraining pipeline built on a prefix trie. Define the full API including build, tokenize, and detokenize methods.
  • How would you handle Unicode in this tokenizer, specifically multi-byte UTF-8 sequences, emojis, and CJK characters?
  • What normalization strategy would you apply before tokenization, and what are the trade-offs of choices like NFKC versus lowercasing?
  • How should the tokenizer handle whitespace and punctuation, and what is your fallback strategy for unknown tokens?
  • Analyze the time and space complexity of the trie-based tokenizer for both build and tokenize operations.
  • How would you support incremental vocabulary updates without rebuilding the entire trie from scratch?
  • What tests would you write to cover tricky tokenizer inputs?
  • Compare the trie-based greedy tokenizer to BPE and WordPiece. What are the throughput and memory trade-offs in a production LLM pipeline?

“This was the core of the whole interview.”

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