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Point72 Asset Management·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Technical phone screen for an ML engineer role at Point72. One question, pretty conceptual, and the interviewer seemed happy with where the conversation went.

Questions Asked (1)

Q1

Given the same input, why might a decoder-based language model produce a different output each time it runs?

Technical Trade-offsSystem Design
Author's notes

Talked through sampling and temperature, how the model draws from a probability distribution rather than always picking the top token.

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AI HintsAI Generated

Suggested Approach

Start by clarifying that the model itself is deterministic given the same input and parameters, but the output can vary due to stochastic sampling techniques used during decoding. Then explain the common sources of randomness, such as temperature, top-k, top-p, and dropout, and how they affect the output distribution. Finally, discuss the trade-offs between deterministic and stochastic decoding in terms of creativity, diversity, and reproducibility.

Pro tip: Mention that in production systems, especially in finance, you often need deterministic outputs for auditing and compliance, so you might set temperature to 0 or use greedy decoding, but this can reduce diversity. Also, note that even with temperature 0, floating-point non-determinism on GPUs can cause slight variations.

1. Clarify the premise

Acknowledge that the model's forward pass is deterministic, but the decoding process can introduce randomness. This sets the stage for explaining why outputs differ.

2. Explain stochastic decoding methods

Describe how sampling techniques like temperature scaling, top-k, and top-p (nucleus) sampling introduce randomness by sampling from a probability distribution rather than always picking the highest-probability token.

3. Discuss other sources of randomness

Mention that dropout during inference (if not disabled) or non-deterministic GPU operations (e.g., due to floating-point rounding) can also cause variations, though these are less common.

4. Highlight trade-offs and use cases

Explain that stochastic decoding is used to increase diversity and creativity, but for tasks requiring reproducibility (e.g., financial modeling), deterministic methods like greedy search or beam search are preferred.

5. Conclude with practical implications

Summarize that understanding these mechanisms allows engineers to control the randomness, balancing between creativity and consistency based on the application.

Key Points to Mention

  • Temperature scaling: higher temperature increases randomness, lower temperature makes output more deterministic.
  • Top-k sampling: restricts sampling to the k most likely tokens, but still random among them.
  • Top-p (nucleus) sampling: samples from the smallest set of tokens whose cumulative probability exceeds p, introducing randomness.
  • Greedy decoding vs. beam search: deterministic methods that always pick the most likely token or sequence.
  • Dropout: if not disabled during inference, it can introduce randomness, but typically it's turned off.
  • Floating-point non-determinism: GPU operations can produce slightly different results due to parallel reduction order, affecting reproducibility.

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