Portfolio Overview & Project Selection Questions
Be ready to describe your strongest 2-3 projects concisely. For each, explain the problem you solved, your design approach, tools used, and outcomes or learnings. Even if your projects are academic or hypothetical, focus on your thinking process and how you would measure success.
MediumTechnical
0 practiced
Discuss how you ensure experiment and model reproducibility in projects you include in your portfolio. Mention tools and practices you use such as reproducible environments, experiment tracking, data versioning, and CI/CD for models.
MediumTechnical
0 practiced
Describe a project where you had to optimize inference latency and throughput for production. Explain the profiling steps you performed, model changes you made (for example quantization, pruning, distillation), engineering work (batching, async pipelines, caching), and how you measured the trade-off between speed and accuracy.
MediumTechnical
0 practiced
Tell me about the most surprising result you encountered in any portfolio project. How did you notice the anomaly, what investigation steps did you take (experiments, logging, data checks), what was the root cause, and how did you adapt the project or model based on your findings?
EasyBehavioral
0 practiced
Describe a time you hit a significant blocker in an ML project included in your portfolio (for example, poor data quality, compute limits, or concept drift). What was the blocker, what steps did you take to resolve or mitigate it, and what was the outcome and lesson learned?
HardTechnical
0 practiced
Describe the most technically difficult experiment or implementation detail from a portfolio project (for example a custom loss function, a distributed training optimization, or a large-scale data pipeline trick). Provide pseudocode or equations, explain why it was necessary, and how you validated that it improved results compared to the baseline.
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