5 exercises — choose the best-structured answer to common AI Safety Engineer interview questions. Focus on RLHF mechanics, red-teaming methodology for LLMs, safety benchmarks and evaluation frameworks, alignment techniques including constitutional AI and DPO, and responsible AI deployment and governance.
Structure for AI Safety Engineer interview answers
Name the technique precisely: RLHF vs DPO vs constitutional AI — explain mechanism, not just the name
Describe the evaluation: what red-teaming tests for, how safety benchmarks are structured (MT-Bench, HarmBench)
"Explain how RLHF works and what its main limitations are."
Option B is best because it names all three stages with precise technical mechanisms (Bradley-Terry preference model, PPO, KL-divergence penalty), explains why the KL penalty exists (preventing degenerate reward-hacking drift), names four distinct limitations with mechanistic explanations including Goodhart's Law, and covers annotator representativeness as a population-level safety concern. Options A, C, and D identify the stages and mention reward hacking but none explains the Bradley-Terry model, the KL penalty purpose, or the Goodhart's Law framing of the RM limitation.
2 / 5
"How do you structure an LLM red-teaming exercise?"
Option B is best because it provides a five-phase structure with named content for each phase, specifies the threat model dimensions (adversary type, harm scope, deployment context), lists a harm taxonomy with six categories including CBRN uplift grading, explains the domain expert team composition rationale, names automated red-teaming tools, specifies the exact logging format for each attempt, and distinguishes systemic failures from edge cases in the output. Options A, C, and D describe red-teaming correctly at a high level but none provides the five-phase structure, the threat model framing, CBRN uplift grading, or the systemic vs edge case output distinction.
3 / 5
"What are the main AI safety evaluation benchmarks and what do they measure?"
Option B is best because it organises benchmarks into three dimensions (harmfulness, honesty, instruction quality), names specific attack methods tested in HarmBench (GCG suffix attacks, many-shot jailbreaking), explains TruthfulQA's specific sycophantic truthfulness failure mode, names additional benchmarks beyond the common four (HaluEval, FActScorer, CValues, JailbreakBench), provides HELM's exact scope (42 scenarios, 5 metric dimensions), and identifies benchmark contamination as the shared limitation with a deployment-specific mitigation. Options A, C, and D name the key benchmarks but none explains the attack methods, the sycophantic failure mode, contamination risk, or organises them into a dimensional framework.
4 / 5
"Compare RLHF and Direct Preference Optimisation (DPO) as alignment techniques."
Option B is best because it explains DPO's mathematical foundation (closed-form mapping from optimal RL policy to reward function, Bradley-Terry assumption), describes the DPO loss function precisely (binary cross-entropy on log-probability ratios), names three specific DPO limitations with mechanisms (noisy label sensitivity, implicit reward inaccessibility, IPO as a variant for near-identical pairs), and gives a practical summary of when each is preferred (DPO for instruction tuning, RLHF when reward interpretability matters). Options A, C, and D correctly state that DPO removes the reward model but none explains the mathematical equivalence, the log-ratio loss, or the IPO variant.
5 / 5
"How do you design a responsible AI deployment framework for a production LLM?"
Option B is best because it names all five layers with specific technical mechanisms (input classifiers for prompt injection AND jailbreaks, harm classifier with confidence threshold for human review queue, anomaly detection for classifier rejection spikes), specifies the model card content, names high-risk domain grounding checks with disclaimers, covers data minimisation as part of logging governance, and describes a concrete governance model with a five-stakeholder review board and a three-tier escalation path. Options A, C, and D identify the main components correctly but none explains the anomaly detection pattern, the three-tier escalation path, data minimisation, or the confidence threshold human review queue.