Amazon·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon Applied Scientist interview that went deep on computer vision work. The whole thing was basically a technical interrogation of your past projects, so if you're not ready to defend every architectural choice you made, it will show.

Questions Asked (3)

Walk me through a computer vision project you've worked on in detail: what model architecture did you use, how did you handle dataset collection and labeling, and how did you set up training and evaluation?

Technical Trade-offsSystem Design

Author's notes

This is the kind of question that sounds easy until you realize they want the full picture, not a highlight reel.

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AI HintsAI Generated

Suggested Approach

Choose a project where you made key decisions and can quantify results. Structure your answer to cover the full ML lifecycle: problem definition, data, model, training, evaluation, and deployment. Emphasize trade-offs and learnings, aligning with Amazon's customer obsession and bias for action.

Pro tip: Quantify the impact of your project (e.g., accuracy improvement, cost savings) and explicitly discuss trade-offs you considered, such as model complexity vs. inference latency. This shows you think like an owner and can make data-driven decisions.

1. Set the context and problem

Briefly describe the business problem, the goal of the computer vision project, and your specific role. Mention constraints like latency, cost, or accuracy targets.

2. Explain data collection and labeling

Detail how you gathered and labeled data, including sources, volume, preprocessing, and any tools or pipelines used. Discuss challenges like class imbalance or labeling quality.

3. Describe model architecture and rationale

Explain the model architecture chosen (e.g., CNN, transformer) and why it suited the problem. Mention any customizations or comparisons with alternatives.

4. Outline training setup and evaluation

Cover training details: framework, hardware, hyperparameters, augmentation, and regularization. Describe evaluation metrics, validation strategy, and how you ensured robustness.

5. Highlight results and learnings

Share quantitative outcomes (e.g., accuracy, F1, latency) and key takeaways. Discuss what you would do differently and how it impacted the business.

Key Points to Mention

Model architecture choice and trade-offs (e.g., accuracy vs. inference speed)
Data collection methods, labeling tools, and quality control
Training infrastructure (e.g., distributed training, GPU usage) and hyperparameter tuning
Evaluation metrics (e.g., precision/recall, mAP) and validation techniques
Deployment considerations (e.g., model optimization, serving latency)
Quantified business impact and lessons learned

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

What were the key results of your computer vision project, and what failure modes did you observe during development or deployment?

Root Cause AnalysisProduct Analytics & Metrics

Author's notes

They weren't satisfied with just metrics.

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AI HintsAI Generated

Suggested Approach

Start by quantifying the project's impact with concrete metrics (e.g., accuracy, latency, cost savings) and then discuss failure modes using a structured root cause analysis. Connect each failure mode to the actions taken to mitigate or resolve it, emphasizing learnings and improvements.

Pro tip: Amazon values customer obsession and ownership; frame your results in terms of customer impact and show how you took ownership of failure modes by implementing long-term fixes, not just quick patches.

1. Set the context

Briefly describe the computer vision project, your role, and the business objective to orient the interviewer.

2. Highlight key results

Present 2-3 quantifiable outcomes (e.g., accuracy improvement, latency reduction, cost savings) and tie them to business impact.

3. Identify failure modes

Describe specific failure modes observed during development or deployment, such as data drift, edge cases, or model degradation.

4. Perform root cause analysis

Explain how you diagnosed each failure mode, using tools like error analysis, monitoring, or A/B testing to find the underlying cause.

5. Share resolutions and learnings

Detail the actions taken to fix the issues, the results of those actions, and what you learned to prevent future occurrences.

Key Points to Mention

Quantifiable metrics (e.g., precision/recall, F1 score, inference time, cost per prediction)
Specific failure modes (e.g., overfitting, class imbalance, distribution shift, adversarial attacks, hardware limitations)
Root cause analysis techniques (e.g., 5 Whys, fishbone diagram, confusion matrix analysis)
Mitigation strategies (e.g., data augmentation, model retraining, ensemble methods, monitoring alerts)
Business impact (e.g., improved customer experience, reduced manual review, increased revenue)
Ownership and learnings (e.g., implemented long-term fixes, documented best practices, shared with team)

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

Looking back at your computer vision work, what would you change about the technical decisions you made?

Technical Trade-offsAdaptability & Ambiguity

Author's notes

Retrospective question that I fumbled a little.

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AI HintsAI Generated

Suggested Approach

Choose a specific computer vision project where a technical decision had clear trade-offs, and reflect on what you learned. Focus on the decision-making process and how you would apply those lessons to future work, showing growth and self-awareness.

Pro tip: Emphasize the trade-offs you considered at the time and how your perspective has evolved—Amazon values candidates who can balance short-term constraints with long-term scalability and customer impact.

1. Set the context

Briefly describe the computer vision project, your role, and the technical decision you want to revisit. Keep it concise to focus on the reflection.

2. Explain the original decision

State what you chose and why, including the constraints (e.g., time, data, compute) that influenced you. This shows you understand the trade-offs.

3. Analyze the outcome

Discuss the results—what worked, what didn’t, and any unintended consequences. Use metrics or specific examples if possible.

4. Propose an alternative

Describe what you would change and why, considering new knowledge or technologies. Explain how this would improve outcomes.

5. Extract lessons learned

Summarize the broader principles you took away and how you’ve applied them since, showing adaptability and growth.

Key Points to Mention

Specific technical decision (e.g., model architecture, data augmentation, deployment strategy)
Trade-offs considered (e.g., accuracy vs. latency, cost vs. scalability)
Constraints at the time (e.g., limited data, tight deadline, hardware limitations)
Measurable impact of the original decision (e.g., accuracy drop, increased inference time)
Alternative approach and its potential benefits (e.g., using a different framework, optimizing preprocessing)
Lessons applied to subsequent projects (e.g., prototyping early, investing in data quality)

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