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Inception·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Technical screen for an ML Engineer role at Inception. The main problem was implementing autoregressive text generation in PyTorch from scratch, covering greedy decoding up through nucleus sampling. Pretty dense for a single question but it's the kind of thing you either know cold or you don't.

Questions Asked (1)

Q1

Implement an autoregressive text generation function in PyTorch that supports greedy decoding, temperature sampling, top-k sampling, and top-p (nucleus) sampling. The function should stop when a max token budget is hit or when all sequences in the batch have produced an end-of-sequence token.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

This looked manageable at first glance and then I got into the top-p part and my brain started leaking.

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AI HintsAI Generated

Suggested Approach

Start by outlining the autoregressive generation loop, then detail the decoding strategies and stopping conditions. Emphasize how to implement each sampling method efficiently in PyTorch, including handling batched sequences and tracking completion.

Pro tip: Mention that you would use a boolean mask to track finished sequences and avoid unnecessary computation on them, which is crucial for performance in batched generation.

1. Define the generation loop

Set up a loop that runs until max tokens or all sequences are finished. At each step, get logits for the next token from the model.

2. Apply decoding strategy

Based on the chosen method (greedy, temperature, top-k, top-p), transform the logits and sample the next token. Ensure proper handling of batched inputs.

3. Update sequences and track completion

Append the sampled token to each sequence. Update a mask to mark sequences that have generated the EOS token, and stop when all are done.

4. Handle stopping conditions

Check if max token budget is reached or if all sequences have produced EOS. Return the generated sequences, optionally excluding padding.

Key Points to Mention

  • Greedy decoding: select argmax of logits.
  • Temperature sampling: scale logits by temperature before softmax.
  • Top-k sampling: keep only top k logits, set others to -inf, then sample.
  • Top-p (nucleus) sampling: keep smallest set of tokens whose cumulative probability exceeds p, then sample.
  • Batched generation: use masks to handle finished sequences and avoid unnecessary computation.
  • Stopping conditions: max token budget and EOS token detection for all sequences.

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