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xAI interview notes, page 5 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·Onsite - System Design / Architecture

May 2026
System design round at xAI focused entirely on database fundamentals, specifically ACID properties. The depth they expected was pretty serious for what felt like a foundational topic.
  • Walk me through the ACID properties of database transactions. What does each one mean, and how do real systems actually enforce them?

“I started with the textbook definitions which was fine, but then they pushed on the enforcement side and that's where I started sweating.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Technical phone screen for a software engineer role at xAI, pretty much one deep question about memory management in C++ and Rust. The question had a lot of surface area and I think I covered the basics but got a bit lost in the weeds on reference cycles.
  • What are smart pointers, how do they handle ownership and lifetimes versus raw pointers, and can you walk through the common types and their tradeoffs including pitfalls like reference cycles?

“This is one of those questions that sounds contained but keeps expanding.”

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xAI·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at xAI for a software engineer role, basically one long deep-dive into rate limiting. The question sprawled across algorithms, distributed systems, and multi-region edge cases, which was a lot to cover in one session.
  • Design a rate limiter that enforces a per-client request quota, covering algorithm choices, distributed implementation, and multi-region concerns.

“I started with token bucket because it felt like the safest pick and I could explain it clearly, but the interviewer kept pushing on the trade-offs vs sliding window counter.”

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xAI·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a software engineer role at xAI. Two questions, both pretty standard but the second one requires more prep than people give it credit for.
  • Walk me through the most technically challenging problem you've worked on. What made it difficult, how did you approach it, and what came out of it?
  • What draws you to this company and this team specifically?

“I had a decent story ready but fumbled the "what made it hard" part.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a software engineering role at xAI and got hit with a classic kth largest element problem. They pushed pretty hard on the tradeoffs between different approaches, especially around what happens when your input doesn't fit in memory.
  • Given an unsorted array of integers and a value k, find the kth largest element. Walk through multiple approaches and their tradeoffs, including what you'd do if the array is too large to fit in memory.

“Started with sorting, which they let me finish before asking 'can we do better?' Moved to a min-heap of size k which felt cleaner.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a software engineering role at xAI. One question, behavioral in structure but really technical under the hood. Left feeling like I either nailed it or completely missed what they were looking for.
  • Walk me through the most technically challenging problem you've solved. Cover the context, what made it hard, the approaches you tried, what finally worked and why, and the impact it had.

“This question sounds like a soft opener but it really isn't.”

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xAI·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML Engineer role at xAI and got a coding round that was basically a palindrome problem dressed up in four layers of follow-ups. Felt manageable at first and then very much didn't.
  • Implement a function that checks whether a string is a palindrome after stripping non-alphanumeric characters and normalizing case.
  • How would you handle this palindrome check on a streaming input that doesn't fit in memory, ideally in one pass with sublinear extra space?
  • Modify the palindrome check to allow up to k character deletions and still return true. Start with k=1 and generalize.
  • If the string is not a palindrome, return the index pair of the first mismatch. Walk through time and space complexity, edge cases, and how you'd write unit tests for all of this.

“The base version was fine, two-pointer from both ends after cleaning the string.”

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xAI·Machine Learning Engineer·Technical Phone Screen

May 2026
ML Engineer interview at xAI that was basically one long deep-dive case study on house price prediction. They wanted you to walk through the entire pipeline from raw data to deployed model, no hand-holding. Dense but interesting.
  • You have historical home sales data with features like lot area, year built, room counts, neighborhood, and sale date. Walk through your end-to-end approach to predict sale price for new listings, covering problem framing, data prep, modeling, evaluation, and deployment.
  • How would you handle missing values and outliers in this dataset, and what leakage risks would you watch out for?
  • What feature engineering would you do for the numeric, categorical, time-based, and location features, and are there any interaction terms worth building?
  • What baseline would you start with, and how would you progress through model choices up to ensembles?
  • How would you set up your evaluation protocol and which metrics would you use?
  • How would you handle market shifts and non-stationarity over time, and what would your monitoring and retraining strategy look like?
  • If you had to deploy a lightweight version of this model under strict latency and memory constraints, how would you approach it?
  • What interpretability and fairness considerations would you raise for a home price prediction model?

“This is a monster question and I did not pace myself well.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Data engineering interview at xAI focused almost entirely on analytics modeling for a Discord-like chat platform. The questions were SQL-heavy with a real emphasis on time series gaps and dimensional modeling, which I wasn't fully expecting.
  • Design a fact-dimension data model to support analytics on servers, views, joins, and messages using the provided raw event tables.
  • Write SQL to compute the average weekly number of server_view events across calendar year 2020.
  • Write SQL to calculate the week-over-week change in server_view events over the most recent 12 months.
  • How would you handle missing weeks in a time series query where some weeks have no events at all?
  • Write SQL to return, for every server, the all-time count of server views, server joins, and messages.

“I went with a pretty standard star schema: a date dimension, a user dimension, a server dimension, and then separate fact tables for each event type.”

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xAI·Software Engineer·Technical Phone Screen

May 2026
Did a technical phone screen for a software engineer role at xAI. The whole thing was basically one coding problem about implementing a token bucket rate limiter with lazy refill logic backed by a cache. Pretty focused session, no fluff.
  • Implement a lazy-refill token bucket rate limiter backed by a key-value cache. You need to write a function that brings a bucket's token count up to date given the current timestamp, and a second function that decides whether to allow a request of a given cost, creating a new full bucket for unknown users and always persisting the updated state back to the cache.

“The refill part itself is straightforward, elapsed time times rate, capped at capacity.”

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