Amazon·Machine Learning Engineer·Take-home Assignment·Senior

SeniorPrefer not to say

Jul 2026

Summary

Amazon ML Engineer interview with a single massive take-home style coding question that basically asked you to build an entire shipment delay prediction system from scratch. The scope was genuinely intimidating and I'm not sure I fully appreciated how many sub-problems were buried inside it until I was already knee-deep.

Questions Asked (1)

Given a CSV of shipment events with fields like order ID, origin, destination, ship date, promised date, carrier, weight, item count, scan events, and delivered date, build a full Python ML pipeline from scratch that covers data loading and validation, feature engineering, labeling, model training with cross-validation, probability calibration, feature explanation, and finally outputs a CSV of top at-risk shipments with reason codes. The whole thing needs to run in under 5 minutes on 1 million rows using less than 4 GB of memory.

System DesignTechnical Trade-offsData Modeling

Author's notes

The runtime and memory constraints are what really got me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business objective and constraints, then outline a modular pipeline that addresses each requirement while optimizing for speed and memory. Emphasize trade-offs and justify choices with respect to the 5-minute and 4GB limits.

Pro tip: Use efficient data types (e.g., category for strings, float32 for numerics) and consider chunked processing or out-of-core libraries like Dask or Polars if pandas struggles. Also, leverage approximate algorithms (e.g., histogram-based gradient boosting) to speed up training.

1. Clarify Requirements and Constraints

Ask clarifying questions about the definition of 'at-risk', the desired output format, and any specific reason code requirements. Confirm the environment (e.g., available libraries, single machine) and the evaluation metric.

2. Design Data Loading and Validation

Propose reading the CSV in chunks or using a memory-efficient library like Polars. Outline validation checks for missing values, data types, and consistency (e.g., delivered date >= ship date).

3. Feature Engineering and Labeling

Describe creating features such as transit time, delay, carrier performance, and scan event aggregations. Define the label (e.g., delivered late) and handle class imbalance if necessary.

4. Model Training, Calibration, and Explanation

Choose a fast, scalable model like LightGBM or XGBoost with cross-validation. Apply probability calibration (e.g., isotonic regression) and use SHAP or feature importances for explanation.

5. Output and Performance Optimization

Generate the CSV with top at-risk shipments and reason codes. Discuss optimizations like parallel processing, efficient data structures, and monitoring memory usage to meet the constraints.

Key Points to Mention

Use of efficient data types (e.g., category, float32) and chunked reading to stay within memory limits.
Feature engineering: compute transit time, delay, carrier historical performance, and scan event patterns.
Label definition: binary classification of late delivery, with potential for cost-sensitive learning.
Model selection: LightGBM or XGBoost for speed and scalability, with built-in cross-validation.
Probability calibration: isotonic regression or Platt scaling to ensure reliable probabilities.
Reason codes: use SHAP values or feature importances to explain top predictions.

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