Amazon·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Amazon system design round focused entirely on ML, and they wanted the full picture from one project, not a highlight reel. Pretty grueling if you haven't actually shipped something end-to-end.

Questions Asked (1)

Pick a machine learning project from your resume and walk through the entire lifecycle: how you defined the problem and measured success, where the data came from and how it was labeled, what features and model you chose, how training was set up and on what infrastructure, how you evaluated offline, what your A/B test plan looked like, how you monitored in production, what could go wrong, any privacy or compliance constraints, and how you'd think about cost and scale.

System DesignA/B Testing & ExperimentationTechnical Trade-offs

Author's notes

This is basically one question that takes 45 minutes if you let it.

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AI HintsAI Generated

Suggested Approach

Choose a project where you owned the end-to-end lifecycle and can speak to both technical and business trade-offs. Structure your answer as a narrative that follows the ML lifecycle stages, highlighting key decisions, metrics, and lessons learned. Emphasize how you measured success, iterated, and handled production challenges, aligning with Amazon's customer-obsession and operational excellence.

Pro tip: Quantify the impact of your project (e.g., 'improved click-through rate by 5%') and be ready to discuss what you would do differently next time, showing humility and a growth mindset.

1. Problem Definition and Success Metrics

Clearly state the business problem, how you translated it into an ML problem, and the metrics (offline and online) you used to measure success. Explain how you aligned these with stakeholder goals.

2. Data Sourcing, Labeling, and Feature Engineering

Describe where the data came from, how it was labeled (including any challenges), and the features you engineered. Mention any data quality checks or preprocessing steps.

3. Model Selection, Training, and Infrastructure

Explain the model(s) you chose and why, the training setup (e.g., distributed training, hyperparameter tuning), and the infrastructure used (e.g., AWS services, on-prem). Highlight any trade-offs.

4. Offline Evaluation and A/B Test Plan

Detail how you evaluated the model offline (e.g., cross-validation, holdout set) and the design of the A/B test, including metrics, sample size, and duration. Mention how you ensured statistical validity.

5. Production Monitoring, Risks, and Compliance

Discuss how you monitored the model in production (e.g., drift detection, performance alerts), potential failure modes, and any privacy or compliance constraints. Also cover cost and scaling considerations.

Key Points to Mention

Alignment of ML metrics with business KPIs (e.g., revenue, customer engagement)
Data labeling strategies and quality assurance (e.g., human-in-the-loop, active learning)
Model choice rationale and trade-offs (e.g., interpretability vs. accuracy, latency vs. complexity)
A/B testing best practices (e.g., randomization, control group, guardrail metrics)
Production monitoring and maintenance (e.g., model retraining, drift detection)
Privacy and compliance considerations (e.g., GDPR, data anonymization, bias mitigation)

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