Amazon Machine Learning Engineer Interview Experiences

Machine Learning Engineer

in depth experience of transformers is required and learn to explain the transformers as well. the overall experience was good

• Design an ML experiment tracking and analysis platform.

Machine Learning Engineer

The interview loop was definitely more AI/ML focused than software focused. There was no leetcode really, but I did have to write a simple KNN algorithm from scratch.

• Find the maximum subarray sum.
• How would you write a KNN search using base python?
• Does quantization cost increase bias, variance, or both?

Machine Learning Engineer

Got a verbal offer but didn't match to the original team. I did eventually get matched to the next team, so don't give up if you get a verbal offer

• In K-Nearest Neighbors (KNN), does setting k=1 lead to higher variance or higher bias?
• What methods or metrics do you use to evaluate how well your LLM performs?
• What methods or metrics did you use to evaluate how well your LLM performs?

Machine Learning Engineer

• Tell me about a time when you made short-term sacrifices for long-term gains.
• Why do you want to join Amazon?
• Implement k-means clustering.
• Explain bias-variance tradeoff.
• Explain the differences between linear and logistic regression.

Machine Learning Engineer

• Tell me about a time when you solved a complex problem and how you went about it.
• Tell me about a machine learning project you worked on and its outcome.
• Explain the differences between LSTM and traditional neural networks.
• Construct a binary tree given its preorder and inorder traversals.
• Describe your experience with PyTorch.
• How do you clean and prepare troublesome datasets?
• Given imbalanced training data, how do you prevent overfitting?
• How would you train a model on a large dataset with few labels?
• Explain semi-supervised learning.
• What are some effective loss functions for computer vision models?
• Explain normalization and Bayes' theorem.