OpenAI
Interview Prep

Walk through: the attention mechanism (queries, keys, values — computing similarity scores, normalizing with softmax, weighted value aggregation), multi-head attention (learning different relationship types in parallel), positional encoding (adding position information since attention is permutation-invariant), the encoder/decoder stack, and layer normalization. Explain why self-attention outperforms RNNs: it's O(1) sequential operations (vs. O(n) for RNNs), enabling parallelization and capturing long-range dependencies without vanishing gradients.

Walk through: training infrastructure (distributed training strategy — data parallelism, model parallelism, pipeline parallelism), training stability challenges (loss spikes, gradient issues, checkpoint recovery), evaluation framework (offline metrics + human evaluation), serving infrastructure (inference optimization — quantization, KV cache, batching strategy), and monitoring in production. Be specific about scale: parameters, data, compute.

Address the core challenges: there's no single ground truth (many good answers exist), human evaluation is expensive and inconsistent, automated metrics (BLEU, ROUGE) correlate poorly with human preference for generative tasks, and models can be 'eval-hacked' (optimized for the benchmark rather than the underlying capability). Describe your evaluation approach: task decomposition, rubric design, evaluator calibration, A/B testing in production, and using model-based evaluation (using a stronger model to evaluate a weaker one).

Describe the task and baseline performance, why naive scaling wasn't the path (computational constraints, diminishing returns, fundamental capability gap), what you tried instead (better data curation, improved training objective, architectural modification, prompting strategy, chain-of-thought, RLHF, DPO), and the improvement achieved. Show creative problem-solving within constraints.

Walk through: task definition and evaluation metric selection, data curation (quality vs. quantity trade-off, format for instruction tuning vs. continued pretraining), fine-tuning approach (full fine-tune vs. LoRA/QLoRA for efficiency vs. RLHF for preference alignment), training stability considerations, overfitting prevention (early stopping on held-out eval, data diversity), and production deployment. Address catastrophic forgetting.

Demonstrate genuine engagement with the field: discuss specific problems (scalable oversight — how do we supervise AI systems more capable than we are, reward hacking, deceptive alignment, interpretability — understanding what's happening inside neural networks, distributional shift in deployment). Share your own view on which problems are most tractable and important, and why.

Walk through: the unexpected behavior (wrong predictions, training instability, performance degradation in production), your debugging approach (dataset examination, loss curve analysis, ablations, attention visualization, error analysis by category), what you found, and what you fixed. Show systematic debugging methodology for ML systems.

Capabilities refer to what the model can do (solve math problems, write code, reason logically). Alignment refers to whether the model does what we intend it to do, in ways that are beneficial and honest. A highly capable but misaligned model could be dangerous — it could pursue proxy objectives, be deceptively aligned during training but not deployment, or optimize for human approval rather than actual helpfulness. This distinction matters because we need both — and improving capabilities without improving alignment can increase risk.