Xai·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

xAI interview, looked like an ML infra or systems engineering role based on the question. One meaty coding/design problem about building a batched decoding loop for a language model. Pretty niche but makes sense given what they're building.

Questions Asked (1)

You're given a simulated LLM interface where each call takes a batch of token-prefix sequences and returns the next token per sequence. Implement a dynamic batching loop: maintain a fixed number of active slots and a waiting queue, fill empty slots as sequences finish, route results correctly via a slot-to-sequence mapping, and handle stop conditions and per-sequence callbacks. Also discuss correctness edge cases around sequences finishing at different times versus naive whole-batch decoding.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a minute to even parse.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the interface and constraints, then outline a slot-based batching loop with a waiting queue and slot-to-sequence mapping. Emphasize correctness by comparing dynamic batching to naive whole-batch decoding, highlighting edge cases like sequences finishing at different times and the need for per-sequence callbacks.

Pro tip: Mention that you would instrument the loop with metrics (e.g., slot utilization, queue wait time) to validate performance and catch subtle bugs like slot leaks or misrouted results.

1. Clarify requirements and interface

Ask about the LLM interface: batch size limits, token-prefix format, stop conditions (EOS token, max length), and callback semantics. Confirm whether sequences can be preempted or must run to completion.

2. Design data structures

Define a fixed-size array of active slots, each holding a sequence ID and its current token prefix. Maintain a FIFO waiting queue of pending sequences and a mapping from slot index to sequence ID for result routing.

3. Implement the batching loop

While there are active sequences or waiting sequences: fill empty slots from the queue, call the LLM with the batch of active prefixes, route returned next tokens to the correct sequences via the slot mapping, append tokens, and check stop conditions. On completion, free the slot, invoke the callback, and pull the next waiting sequence.

4. Handle edge cases and correctness

Address sequences finishing at different times: ensure finished sequences are removed immediately so they don't consume slots or receive extra tokens. Compare to naive whole-batch decoding where all sequences must finish together, causing wasted computation and latency for short sequences.

5. Discuss trade-offs and optimizations

Talk about trade-offs: fixed slots vs. dynamic resizing, queue ordering (FIFO vs. priority), and batching efficiency. Mention potential optimizations like continuous batching, speculative decoding, or grouping similar-length sequences.

Key Points to Mention

Slot-to-sequence mapping to correctly route LLM outputs back to the right sequence.
Waiting queue management: FIFO order, backpressure, and avoiding starvation.
Stop conditions: EOS token, max length, or custom criteria; ensure callbacks are invoked exactly once.
Correctness edge cases: sequences finishing at different times, empty batches, and slot reuse without data leakage.
Comparison to naive whole-batch decoding: dynamic batching reduces latency and improves throughput by not waiting for the slowest sequence.
Concurrency and thread-safety if the loop runs asynchronously or with multiple workers.

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