Xai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Interviewed for an ML Engineer role at xAI and got a pretty deep systems-level coding question about building a batched inference engine from scratch. Not your typical LeetCode session, more like a mini-project scoped to an hour.

Questions Asked (1)

Design and implement a dynamic batching decoding engine for a black-box language model interface. The engine should manage a fixed-capacity batch, pull from a waiting queue to fill open slots, handle per-sequence stopping conditions (both a single stop token and a stop sequence suffix), respect per-request token limits, and correctly map batch slots to requests so outputs never get mixed up when slots are refilled.

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 black-box interface constraints (e.g., synchronous calls, no access to logits) and the performance goals. Then outline a slot-based architecture with a request manager, a scheduler that fills slots from the waiting queue, and a per-slot state machine that tracks token limits and stopping conditions. Emphasize correctness of slot-to-request mapping and efficient handling of stop sequences.

Pro tip: Use a generation counter or unique request ID per slot to detect stale outputs when slots are reused, and always validate stop sequences by checking the suffix of the generated text rather than relying on token-level matching.

1. Clarify requirements and constraints

Ask about the black-box interface (e.g., can it generate multiple tokens per call? Is it synchronous?), expected throughput, and whether stop sequences can span multiple tokens. Confirm that per-request token limits include prompt tokens or only generated tokens.

2. Design the core data structures

Define a Request object with fields: id, prompt, max_tokens, stop_token, stop_sequence, generated_tokens, and state. Define a Slot object with: slot_id, current_request_id, and generation counter. Maintain a waiting queue (FIFO or priority) and a fixed-size array of slots.

3. Outline the scheduling loop

In each iteration, fill empty slots by dequeuing requests from the waiting queue. For each active slot, call the black-box model to generate the next token(s). Then, for each slot, check stopping conditions: token limit reached, stop token generated, or stop sequence suffix matched. If stopped, finalize the request, clear the slot, and immediately refill from the queue.

4. Ensure correct slot-to-request mapping

When a slot is assigned to a request, store the request ID and increment a generation counter. When processing model outputs, verify that the slot's current request ID matches the one used for the call, and discard any output if the generation counter changed (indicating the slot was reused).

5. Discuss trade-offs and optimizations

Address trade-offs: batching multiple tokens per call vs. per-token calls, handling stop sequences efficiently (e.g., using a trie or rolling hash), and whether to prioritize short or long requests. Mention potential optimizations like dynamic batch sizing or speculative decoding if applicable.

Key Points to Mention

Slot reuse and stale output prevention via request IDs and generation counters
Efficient stop sequence detection using suffix matching or a finite automaton
Per-request token limit enforcement including prompt and generated tokens
Queue management strategies (FIFO, priority, or fairness) to avoid starvation
Handling of partial stop sequences that may span multiple model calls
Error handling and retries for black-box model failures

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