# Amazon Machine Learning Engineer Interview Experiences Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/guides/amazon-machine-learning-engineer-interview/experiences Source observed: 2026-11-04T22:31:40.775Z Last checked: 2026-11-04T22:31:40.775+00:00 Indexed: 2026-11-05T20:31:18.1+00:00 --- # Amazon Machine Learning Engineer Interview Experiences We provide candidate-reported experiences from the Amazon Machine Learning Engineer interview process. This guide section was updated a month ago by Amazon candidates and contains 7 interview experiences, alongside an expert guide, 78 questions, and resumes. - [Expert guide](https://www.aced.io/guides/amazon-machine-learning-engineer-interview) - [Interview experiences](https://www.aced.io/guides/amazon-machine-learning-engineer-interview/experiences) - [Questions](https://www.aced.io/guides/amazon-machine-learning-engineer-interview/questions) - [Resumes](https://www.aced.io/guides/amazon-machine-learning-engineer-interview/resumes) [Share your experience](https://www.aced.io/questions/contribute?src=guide) to help other candidates prepare. ## Interview overview Based on the reported interviews collected here: - **Difficulty:** Most candidates rate the Amazon interview as **moderate**. - **Outcomes:** **75%** of reported interviews result in an offer: 3 accepted and 1 without an offer. - **Questions asked:** Machine Learning and Concept questions are the most common, with 4 Machine Learning questions, 4 Concept questions, 3 Coding questions, and 9 questions in other categories. ## Candidate experiences ### Applied Scientist [Read the full Applied Scientist experience](https://www.aced.io/experiences/amazon-machine-learning-engineer-interview-20c561?returnPath=%2Fguides%2Famazon-machine-learning-engineer-interview%2Fexperiences) - **Company:** Amazon - **Level:** Mid Level / L4 - **Location:** United States - **Format:** Remote - **Interview month:** September 2026 - **Submitted:** September 30, 2026 - **Status:** In Progress - **Difficulty:** Moderate The candidate said the questions were not difficult but required actual experience, and that preparation could not help much. Questions included: - [What evaluation metrics would you use to evaluate video generation?](https://www.aced.io/questions/7361) - [what to do when users have subjective standards about the generated videos](https://www.aced.io/experiences/amazon-machine-learning-engineer-interview-20c561?returnPath=%2Fguides%2Famazon-machine-learning-engineer-interview%2Fexperiences) ### Additional Machine Learning Engineer experiences Some additional experiences are available in the collection. Their visible summaries and questions include: 1. **Machine Learning Engineer — Amazon, intern, United States** - The interview began with a self-introduction. The interviewer asked about the candidate’s research, papers, and potential internship topics. - Questions included [Ask graduation date](https://www.aced.io/experiences/amazon-machine-learning-engineer-intern-interview-a8d17e?returnPath=%2Fguides%2Famazon-machine-learning-engineer-interview%2Fexperiences), [Leetcode](https://www.aced.io/experiences/amazon-machine-learning-engineer-intern-interview-a8d17e?returnPath=%2Fguides%2Famazon-machine-learning-engineer-interview%2Fexperiences), and ML coding and research. One additional question is indicated but not exposed in the supplied content. 2. **Machine Learning Engineer** - The candidate said in-depth transformer experience was required and that candidates should be able to explain transformers. The overall experience was good. - Question: Design an ML experiment tracking and analysis platform. 3. **Machine Learning Engineer** - The interview loop was more AI/ML-focused than software-focused. The candidate reported no LeetCode interview but did write a simple KNN algorithm from scratch. - Questions included finding the maximum subarray sum, writing a KNN search using base Python, and whether quantization cost increases bias, variance, or both. 4. **Machine Learning Engineer** - The candidate received a verbal offer but was not matched to the original team. The candidate was eventually matched to the next team and advised not to give up after receiving a verbal offer. - Questions included whether setting `k=1` in K-Nearest Neighbors leads to higher variance or higher bias; methods or metrics for evaluating an LLM; and a repeated version of the LLM evaluation question. 5. **Machine Learning Engineer** - Questions included: - 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 the bias-variance tradeoff. - Explain the differences between linear and logistic regression. 6. **Machine Learning Engineer** - Questions included: - 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. The collection includes seven experiences, while only some experience details are available without unlocking the remainder. ## Unlock more experiences You can get trial access by [sharing your own interview experience](https://www.aced.io/questions/contribute?src=guide-experiences), or [become a member](https://www.aced.io/upgrade?src=guide-experiences) to unlock everything. 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