Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Interviewed at OpenAI for what seemed like a research or applied ML engineering role. The question was a two-parter that went deep into probability theory and adversarial system design around LLM decoding. Pretty niche stuff, not your typical coding screen.

Questions Asked (2)

Given a per-step probability distribution over the vocabulary (including the EOS token), analyze the probability distribution of the stopping time during LLM decoding. That is, how many tokens get generated before an EOS or stop sequence is sampled?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is basically a geometric distribution question at its core, but with a twist because stop sequences add memory to the process.

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AI HintsAI Generated

Suggested Approach

Model the stopping time as the first passage time of a non-homogeneous Bernoulli process where the success probability at each step is the probability of sampling EOS or a stop sequence. Derive the distribution using the survival function (probability of not stopping after k steps) and relate it to the product of per-step continuation probabilities. Discuss how this distribution can be computed efficiently and its implications for decoding strategies.

Pro tip: Mention that in practice, stop sequences can be multi-token, so you need to track the probability of the stop sequence being generated at each step, which may require considering overlapping sequences. Also, note that the distribution is not geometric unless the per-step probabilities are constant.

1. Define stopping event

Clarify that stopping occurs when the model samples the EOS token or any token that completes a predefined stop sequence. For multi-token stop sequences, the event is more complex and may depend on previous tokens.

2. Model per-step stopping probability

Let p_t be the probability of stopping at step t given no stop before. For EOS only, p_t is the probability of EOS at step t. For stop sequences, p_t is the probability that the generated sequence up to t ends with a stop sequence.

3. Derive survival function

The probability of generating at least k tokens is S(k) = ∏_{t=1}^{k} (1 - p_t). Then the probability of stopping exactly at step k is P(T = k) = S(k-1) * p_k.

4. Analyze distribution properties

Discuss the mean, variance, and tail behavior. If p_t is constant, T is geometric. Otherwise, it's a non-homogeneous geometric distribution. The expected stopping time is sum_{k>=0} S(k).

5. Consider computational aspects

Explain how to compute or approximate this distribution efficiently, especially for long sequences, and how it can inform decoding strategies like early stopping or beam search.

Key Points to Mention

Non-homogeneous Bernoulli process: each step has a different stopping probability.
Survival function: product of continuation probabilities.
Geometric distribution as a special case when stopping probability is constant.
Expected stopping time as sum of survival probabilities.
Multi-token stop sequences require tracking sequence probabilities.
Implications for decoding: e.g., setting a maximum length or using probability thresholds.

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

Using your analysis of the stopping time distribution, design a decoding or early-stopping program that holds up against adversarial inputs. Consider adversaries that try to extend generation indefinitely, force premature stopping, or trigger a specific stop sequence. What guarantees can your program provide, and what does 'performing well' even mean here?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

The 'what does performing well mean' part is what got me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define what 'stopping time distribution' means in this context (e.g., token generation stopping probabilities) and what 'performing well' entails (e.g., balancing latency, throughput, and robustness). Then, propose a decoding/early-stopping program that uses probabilistic thresholds and fallback mechanisms to handle adversarial inputs, and discuss guarantees like bounded worst-case latency and resistance to forced stopping.

Pro tip: Acknowledge that perfect adversarial robustness is impossible without trade-offs; focus on making attacks costly and detectable, and emphasize monitoring and adaptive thresholds to maintain performance under attack.

1. Clarify the problem and metrics

Define the stopping time distribution (e.g., probability of stopping at each step) and what 'performing well' means: low latency, high throughput, robustness to adversaries, and bounded resource usage. Discuss trade-offs between these metrics.

2. Model adversarial goals

Identify adversary types: those extending generation (e.g., by crafting inputs that avoid stop tokens), forcing premature stopping (e.g., by injecting stop sequences), and triggering specific stop sequences. Consider their capabilities and constraints.

3. Design the decoding/early-stopping program

Propose a program that uses a combination of hard limits (max tokens, time), probabilistic stopping criteria based on the distribution, and anomaly detection (e.g., entropy thresholds, repetition detection). Include fallback strategies like forced stopping after a timeout.

4. Analyze guarantees and trade-offs

Discuss guarantees: worst-case bounded latency, resistance to forced stopping (e.g., by ignoring user-provided stop sequences or validating them), and detection of adversarial patterns. Acknowledge that no system is perfectly robust; quantify trade-offs between robustness and performance.

5. Propose evaluation and adaptation

Suggest how to evaluate the program against adversaries (e.g., red-teaming, simulation) and how to adapt thresholds dynamically based on observed behavior. Mention monitoring and logging for post-hoc analysis.

Key Points to Mention

Stopping time distribution: model as a probability distribution over stopping steps, possibly conditioned on context.
Adversarial strategies: extending generation (e.g., adversarial suffixes), forcing premature stopping (e.g., stop token injection), triggering specific sequences (e.g., prompt injection).
Guarantees: bounded worst-case latency, probabilistic robustness, and detection mechanisms.
Trade-offs: robustness vs. latency, false positives vs. false negatives in stopping.
Adaptive thresholds: dynamically adjust stopping criteria based on input characteristics or anomaly scores.
Evaluation: use adversarial testing, measure attack success rate, and monitor performance under attack.

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