No small talk, no 'tell me about yourself.' They picked a project off my resume and just started asking.
Select a project where you made significant technical decisions and can clearly articulate the problem, your approach, and the impact. Structure your answer using a narrative arc: context, challenge, action, result, and reflection, emphasizing trade-offs and how you navigated ambiguity. Keep it concise but detailed enough to demonstrate depth and ownership.
Pro tip: Quantify the impact of your work (e.g., latency reduction, accuracy improvement, cost savings) and explicitly discuss a trade-off you made and why, as Amazon values data-driven decisions and customer obsession.
Briefly describe the project's goal, your role, and the business or customer problem it addressed. Mention the team size and timeline to give scope.
Explain the specific ML problem, including data characteristics, constraints (e.g., latency, scalability), and any ambiguities you faced. Highlight why it was non-trivial.
Walk through your technical decisions: data preprocessing, model selection, training, evaluation, and deployment. Focus on why you chose certain methods over alternatives, mentioning trade-offs.
Quantify the outcomes (e.g., accuracy, latency, cost savings) and how they benefited the business or customers. If possible, mention how the solution was adopted or scaled.
Discuss what you would do differently, how you handled ambiguity, and what you learned. This shows growth and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by directly answering that the unit is the bit (when using base-2 logarithms), then briefly explain that surprisal measures the information content of an event and is defined as -log2(p). Connect it to related concepts like entropy and cross-entropy to show depth, and mention that other bases yield different units (e.g., nats for natural log).
Pro tip: Emphasize that the choice of base is a convention, and in most software engineering contexts (e.g., compression, ML loss functions) base-2 is standard, so the unit is bits. This shows you understand both theory and practical applications.
State clearly that the unit of surprisal is the bit when using base-2 logarithm. If other bases are used, mention nats (base e) or hartleys (base 10).
Explain that surprisal (also called self-information) quantifies the amount of information gained from observing an event. It is defined as I(x) = -log_b(p(x)), where p(x) is the probability of the event.
Mention that the expected surprisal over all outcomes is the Shannon entropy, which measures the average information content of a random variable.
Give a practical example: in data compression, the optimal code length for a symbol is its surprisal in bits; in machine learning, cross-entropy loss uses log base e (nats) but is proportional to bits.
Conclude that the unit depends on the logarithm base, but bits are most common in computer science contexts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They kept coming back to this after every answer I gave.
Use the STAR method to structure your answer, focusing on the specific modeling choices you made and the evaluation metrics you used. Explain the trade-offs you considered, such as performance vs. interpretability, and how you validated the model to ensure it met business goals. Highlight any iterations or learnings from the evaluation process.
Pro tip: Quantify the impact of your modeling choices and evaluation results (e.g., 'improved accuracy by 15% while reducing inference time by 20%') to demonstrate business acumen. Also, mention how you aligned the model evaluation with Amazon's customer obsession and operational excellence principles.
Briefly describe the project, its business objective, and the problem you were solving. Mention any constraints like latency, cost, or data availability that influenced your modeling decisions.
Detail the models you considered and why you chose the final one. Discuss trade-offs such as complexity vs. interpretability, training time vs. accuracy, and how you tailored the choice to the project's needs.
Outline the metrics you used (e.g., precision, recall, AUC, RMSE) and why they were appropriate. Explain how you split data, handled validation, and ensured the model generalized well.
Present the evaluation results, including any A/B tests or offline vs. online performance. Mention how you iterated based on findings, such as feature engineering or hyperparameter tuning.
Summarize the business impact (e.g., improved customer experience, cost savings) and key takeaways. Reflect on what you would do differently and how it informs your future work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.