Amazon·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Amazon ML Engineer loop, system design round focused entirely on GenAI. One question, but it was basically a full dissertation prompt crammed into a single ask. Walked out not totally sure if I nailed it or just survived it.

Questions Asked (1)

Walk us through a GenAI system you built from scratch. Cover the problem definition, data sourcing and governance, model selection, training or fine-tuning setup, evaluation, safety and guardrails, latency and cost constraints, and major failure modes. What trade-offs did you make, and how would you scale it to 10x traffic while hitting a 200ms p95 latency target and cutting costs by 20%?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This question is basically five questions duct-taped together.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a narrative that walks through the entire lifecycle of the GenAI system, from problem definition to scaling. Emphasize the trade-offs you made at each stage and how they impacted the final solution. Conclude by outlining a concrete plan to scale to 10x traffic while meeting latency and cost targets, highlighting specific techniques and their expected impact.

Pro tip: Quantify the impact of your decisions—e.g., 'By using X, we reduced latency by Y% and cost by Z%'—and be honest about what you would do differently with hindsight. This demonstrates engineering maturity and a results-oriented mindset.

1. Define the Problem and Success Metrics

Clearly state the business problem, why GenAI is the right solution, and the key performance indicators (KPIs) such as accuracy, latency, cost per query, and safety thresholds. Align these with stakeholder expectations.

2. Data Sourcing, Governance, and Model Selection

Describe how you sourced and curated data, ensured compliance and privacy, and selected the base model (e.g., open-source vs. proprietary) based on trade-offs like performance, cost, and customization needs.

3. Training/Fine-Tuning, Evaluation, and Guardrails

Explain your fine-tuning approach (e.g., LoRA, full fine-tuning), evaluation metrics (both automated and human), and safety mechanisms (e.g., content filters, prompt engineering, fallback rules) to mitigate risks.

4. Deployment, Latency, and Cost Optimization

Detail the deployment architecture, including model serving, caching, batching, and hardware choices. Discuss how you measured and optimized latency and cost, and the trade-offs involved.

5. Scaling Strategy for 10x Traffic with 200ms p95 and 20% Cost Reduction

Propose a scaling plan that includes techniques like model distillation, quantization, caching, autoscaling, and multi-region deployment. Explain how each contributes to meeting latency and cost targets.

Key Points to Mention

Trade-offs between model size, latency, and cost (e.g., using smaller distilled models for high-traffic endpoints).
Data governance: PII redaction, consent management, and bias mitigation.
Evaluation metrics: BLEU/ROUGE for generation, human-in-the-loop for quality, and A/B testing for business impact.
Safety guardrails: input/output moderation, prompt injection defenses, and fallback to rule-based systems.
Latency optimization: model quantization, ONNX runtime, TensorRT, and edge caching.
Cost reduction: spot instances, serverless inference, and request batching.

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