Xai·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

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.

Questions Asked (1)

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.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

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

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then outline the lazy-refill algorithm and the decision logic. Write pseudocode for both functions, emphasizing atomicity and cache interactions, and discuss trade-offs like precision vs. performance.

Pro tip: Mention that you would use a cache operation like GETSET or a Lua script to ensure atomic read-modify-write, preventing race conditions in distributed environments.

1. Clarify Requirements

Ask about expected throughput, cache type (e.g., Redis), consistency needs, and whether the rate limiter is per-user or global. Confirm the token bucket parameters (capacity, refill rate) and cost semantics.

2. Design Data Model

Define the bucket state stored in cache: token count and last refill timestamp. Decide on key naming (e.g., 'bucket:{userId}') and consider using a hash or separate keys.

3. Implement Lazy Refill

Write a function that, given current time, computes elapsed time since last refill, adds tokens at the refill rate, caps at capacity, and updates the timestamp. Ensure it handles missing buckets by initializing a full bucket.

4. Implement Request Decision

Write a function that refills the bucket, checks if tokens >= cost, deducts cost if allowed, and persists the updated state. Always write back to cache, even on denial, to keep the refill timestamp current.

5. Address Concurrency and Edge Cases

Discuss atomicity (e.g., using Redis transactions or Lua scripts), handling clock skew, and ensuring idempotency. Mention fallback strategies if cache is unavailable.

Key Points to Mention

Lazy refill: compute tokens on demand based on elapsed time, avoiding background timers.
Token bucket parameters: capacity, refill rate, and cost per request.
Cache operations: GET, SET, and atomic read-modify-write (e.g., Redis WATCH/MULTI or Lua).
Handling unknown users: create a new bucket with full capacity.
Persisting state: always write back updated token count and timestamp, even if request is denied.
Trade-offs: precision vs. performance, distributed consistency, and failure modes.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.