Artificial Intelligence Projects and Problem Solving Questions

Detailed discussion of artificial intelligence and machine learning projects you have designed, implemented, or contributed to. Candidates should explain the problem definition and success criteria, data collection and preprocessing, feature engineering, model selection and justification, training and validation methodology, evaluation metrics and baselines, hyperparameter tuning and experiments, deployment and monitoring considerations, scalability and performance trade offs, and ethical and data privacy concerns. If practical projects are limited, rigorous coursework or replicable experiments may be discussed instead. Interviewers will assess your problem solving process, ability to measure success, and what you learned from experiments and failures.

HardSystem Design

0 practiced

Architect a distributed training system to train a 100B-parameter transformer across 1,024 GPUs. Discuss data parallelism vs model/pipeline parallelism, optimizer-state sharding (ZeRO), gradient accumulation, checkpointing frequency and format, network topology and bandwidth needs, failure recovery, and how to minimize training wall-clock time.

EasyBehavioral

0 practiced

Tell me about a time you disagreed with a product stakeholder about the feasibility or expected value of a machine learning feature. Describe the disagreement, the data or experiments you ran (if any), how you communicated trade-offs, and what the eventual outcome was.

EasyTechnical

0 practiced

Describe the steps you'd take to collect and validate labels for a supervised learning problem when labeling is expensive (human annotation). Cover sampling strategy, labeling guidelines, quality checks, inter-annotator agreement metrics, and how you'd decide when to stop labeling or scale labeling.

MediumTechnical

0 practiced

In Python, implement expected_calibration_error(probs: List[float], labels: List[int], n_bins: int) -> float to compute the ECE using equal-width bins. Explain how you handle empty bins and the complexity of your implementation. Also describe how you'd produce a reliability diagram as part of model evaluation.

HardSystem Design

0 practiced

Design a real-time recommendation system that must return a personalized ranked list in under 100ms tail latency for 100M daily active users. Discuss online feature store design, candidate generation, ranking model serving, caching strategies, freshness requirements, cold-start solutions, and how you would measure and optimize tail latency.

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