Applied Scientist Interview Questions

Showing 31–34 of 34 results

• EeBayEasy·Applied Scientist
  Design fast query-to-listing retrieval over tens of millions of listings

  An ML system design question on retrieving relevant listings for free-text search queries across a catalog of tens of millions of items with low latency. It tests candidate generation, indexing, embedding training, freshness, filtering and scaling, plus the offline and A/B test metrics used to evaluate retrieval.

  ML System DesignTechnical Screen
• EeBayEasy·Applied Scientist
  Docker optimization, container fundamentals, and CI/CD for an ML model service

  Practice explaining how Docker images, layers, build caching and container isolation work, then optimizing the image and runtime of a Python ML inference service. The question also covers continuous integration, delivery and deployment for shipping a containerized model safely.

  Software Engineering FundamentalsTechnical Screen
• EeBayEasy·Applied Scientist
  Named entity recognition: framing, algorithm choice, data processing, and metrics

  A machine learning question on named entity recognition for short e-commerce text such as listing titles and search queries. It asks you to define the task, compare NER algorithm families and choose one, plan labeling and data processing, and select entity-level and product-level metrics.

  Machine LearningTechnical Screen
• EeBayEasy·Applied Scientist
  Optimizing BERT inference latency and throughput for a production NLP service

  An ML engineering question on speeding up a fine-tuned BERT encoder that has become too slow or too costly to serve in production. It tests profiling, model compression, runtime and serving optimizations such as batching and padding control, and how to verify that accuracy does not regress.

  Machine LearningTechnical Screen