Amazon Machine Learning Engineer Interview Guide (2024)

Amazon Machine Learning Engineer Interview Guide (2024)Amazon Machine Learning Engineer Interview Guide (2024)


Known for its pioneering role in e-commerce and cloud computing, Amazon has established itself as a leader in these domains. Their mission is to be “Earth’s most customer-centric company” where people can find and discover anything they want to buy online.

Their main business strategy is to expand their Machine Learning and Artificial Intelligence capability to continue to dominate markets globally.

If you’re looking for guidance to ace your interview, you’ve come to the right place. This Amazon Machine Learning & AI interview guide is designed to help you navigate every step of the process.

By the end of this guide, you will be armed with the right strategies to succeed in your interview. We will cover the interview process, commonly asked questions, and our favorite tips for you to stand out as a candidate.

What Is the Interview Process Like for a Machine Learning Role at Amazon?

This role requires deep knowledge of machine learning, data structures, and algorithms. Apart from theoretical concepts, the interviewers will test how well you can implement these skills in real-world scenarios. Finally, cultural fit is of huge importance, so make sure to practice common behavioral questions too.

Please note that the questions and structure of the interview process will differ based on the type of machine learning role; ML scientists and engineers have a similar domain but different functions. Always read the job description carefully while preparing your interview plan.

The process generally has multiple rounds spanning several weeks.

Step 1: Preliminary Screening

A phone interview with a recruiter will be scheduled to get a sense of your work experience and skillsets. They may also ask you why you want to join Amazon or ask CV-based questions, so prepare some pointers to help you sail through this important step.

Step 2: Online assessment

You may be asked to solve a set of data structure and algorithms (DSA) questions within a given time. You’ll be able to compile your code multiple times before you submit it.

Step 3: Technical Interviews

Successful candidates then undergo one or two technical interviews, usually via video chat with a potential teammate or manager. These involve both technical (data structure and algorithm questions) and behavioral components, focusing on Amazon’s Leadership Principles.

You can ask your recruiter to share the topics or problem statements that Amazon or the specific team is trying to solve to gain a better idea of the kind of specific problems you should practice for this stage. You can view this page to see various software development issues that the company is solving.

Step 4: Onsite Interviews

If you do well in the online technical rounds, you will be invited for an onsite visit. You will spend a day at one of their offices, participating in four or five rounds and speaking with managers, peers, and a senior executive. These interviews consist of coding, machine learning design, systems design, and behavioral questions.

Step 5: Bar Raiser Interview

One of the final interviews is with a “Bar Raiser” - an interviewer specially trained to maintain high hiring standards at Amazon. They will focus more on your holistic fit in Amazon rather than specific team needs.

Bar Raisers hold positions that are typically outside of the business for which a candidate is being interviewed, and they primarily ascertain that Amazon’s Leadership Principles will be upheld if you are chosen. Read more about this unique interview step here.

What Questions Are Asked in an Amazon Machine Learning Interview?

In this section, we’ll go through the various interview questions that might be asked during an Amazon machine learning interview.

You can expect a mix of technical and behavioral questions. Technical questions often cover fundamental machine learning concepts, like explaining different ML models, understanding the bias-variance tradeoff, and addressing overfitting.

They also include systems design questions, such as solving algorithmic challenges and scaling a recommender system. For the behavioral questions, the interviewers see if you are aligned with Amazon’s Leadership Principles, assess your past projects and conflict resolution, and understand your motivation for joining Amazon.

The aim is to assess not only the candidate’s technical expertise in machine learning but also their problem-solving approach and cultural fit within the organization.

1. How would you interpret coefficients of logistic regression for categorical and boolean variables?

As Amazon strives to improve its classifiers and predictive models, this question explores your depth of knowledge to improve these systems.

How to Answer:

Discuss the interpretation of logistic regression coefficients in the context of a typical Amazon business problem. Emphasize understanding the relationship between these variables and the predicted variable.


“To interpret the coefficient of a categorical variable, you can consider its exponentiated value, which gives us the odds ratio. An odds ratio greater than 1 indicates that the presence of that category increases the odds of the binary outcome. An odds ratio of less than 1 indicates that the presence of that category decreases the odds of the binary outcome relative to the reference category. The magnitude of the odds ratio represents the strength of the association between the categorical variable and the binary outcome.”

2. How would you encode a categorical variable with thousands of distinct values?

Encoding a categorical variable with thousands of distinct values in an Amazon context involves handling large-scale product data efficiently.

How to Answer

You should discuss methods that manage high cardinality while preserving meaningful information for modeling. Consider the computational efficiency and the impact on model performance.


“In scenarios with high-cardinality categorical variables like product IDs, one approach is to use frequency encoding. This method replaces each category with its frequency, which is computationally efficient and can highlight common categories. Another approach is target encoding, where categories are replaced by the average outcome for that category. This can be insightful when predicting customer behaviors or trends. In deep learning contexts, Entity Embedding can efficiently handle high cardinality while capturing complex relationships within the data.”

3. How would you build a model to detect fraud on Amazon’s platform?

Amazon is always optimizing its Fraud Detector to tag potentially fraudulent activities such as payment fraud and fake account creation. This question tests your understanding of the complex nature of e-commerce transactions and user behaviors.

How to Answer

Focus on detailing a robust model to identify fraudulent patterns. Discuss the importance of a comprehensive dataset, feature engineering, and choosing the right algorithm.


“I’d start by gathering a dataset encompassing transaction details, user behavior, and account information. Feature engineering is key, so I’d include variables like transaction frequency, amount irregularities, and shipping-billing address mismatches. Given the complexity of fraud patterns, an ensemble method like Random Forest or Gradient Boosting might be more effective in handling a variety of data types and their robustness against overfitting. Additionally, anomaly detection algorithms could help identify outliers that indicate potential fraud.”

4. How would you choose between two models of 85% and 82% accuracy?

This question tests your understanding of model effectiveness in real-world scenarios, crucial in a workplace like Amazon where nuanced optimizations are directly tied to business goals.

How to Answer

One of the biggest clarifying questions here is the kind of problem being solved. Discuss the importance of other metrics like precision, recall, and AUC curve. Evaluate the models based on the nature of the problem and the cost of errors.


“If it is a classification problem, then accuracy in itself is not a sufficient metric to define the efficacy of the model. I would also look at the distribution of the data. I’d also consider factors like precision and recall, especially in contexts like fraud detection where false negatives are costly. If the 85% accuracy model has a lower recall, it might miss more fraudulent cases than the 82% model. Additionally, I’d assess the models for overfitting and their performance on a validation set. The model’s complexity and the computational resources required are also important, especially for a large-scale platform like Amazon.”

5. If you were building a prediction model for Amazon, which would you pick between linear regression and random forest regression?

Predictive modeling is essential for Amazon to estimate product demand and price optimization. This question tests your knowledge of which model would yield the most reliable results.

How to Answer

Compare the features of linear and random forest regression, considering Amazon’s diverse and non-linear price determinants, and justify the choice of model.


”For Amazon’s diverse and complex data, often with non-linear patterns like customer purchasing behavior or product recommendations, a random forest regression would generally be more effective. It can capture more intricate relationships in the data compared to linear regression.”

6. Let’s say we are trying to improve our search feature. How would you improve recall without changing the underlying algorithm?

Improving search dynamically is a key aspect of Amazon’s e-commerce business. This interview question assesses your knowledge of their platform and ability to think critically.

How to Answer

Focus on methods that enhance data quality or modify the search process’s parameters to increase recall, emphasizing understanding of search mechanisms.


“Recall is the ratio between the number of correct predictions and the number of predictions that were denoted as right. To improve recall in Amazon’s search feature, it would be necessary to enhance prediction by either changing the acceptance threshold or increasing the number of parameters to be evaluated. I would enrich the metadata and product descriptions to ensure broader coverage of relevant keywords. Additionally, adjusting the threshold for matching query terms can increase the number of returned results.”

7. How would you build a product recommender system from the beginning?

This is a systems design question on a topic that is at the heart of Amazon’s personalized customer experience focus.

How to Answer

Detail the steps in building a recommender system, including data collection, choosing the right algorithm (like collaborative filtering or content-based filtering), and evaluating the system’s performance.


“To build a recommender system from scratch, I’d start by collecting user interaction data, such as past purchases and ratings. I would likely use a collaborative filtering approach to find similarities between users and products. The system would be iteratively tested and refined based on metrics like click-through rates and conversion rates to ensure it effectively suggests products that align with individual user preferences and behavior patterns.”

8. In which case would you use a bagging algorithm versus a boosting algorithm?

This question assesses your understanding of ensemble methods and their appropriate application in different scenarios.

How to Answer

Discuss the differences between bagging and boosting algorithms and their suitability based on model variance, bias, and data specifics.


“I would choose a bagging algorithm like Random Forest in scenarios with high variance and overfitting issues, as it helps in reducing variance without increasing bias. Conversely, for cases with high bias or underfitting, a boosting algorithm like XGBoost would be appropriate, as it sequentially builds models to focus on and correct the errors of previous ones, thereby reducing bias.”

9. Which activation function would you choose in a neural network to classify images of different types of chairs?

Image classification is a key development that Amazon is working on to enhance customer experience, streamline product searches, and improve inventory management through efficient categorization.

How to Answer

Explain the characteristics of ReLu and Tanh activation functions and why one might be more suitable for image classification tasks.


“For classifying images of chairs at Amazon, I would choose the ReLu (Rectified Linear Unit) activation function for the hidden layers. ReLu is generally preferred in deep learning for image classification because it helps in faster training and mitigates the vanishing gradient problem, which is common with Tanh in deeper networks. Its ability to provide a non-linear transformation with a simpler gradient propagation means it is better for handling complex patterns in image data.”

10. Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Predictive modeling through ensemble methods is a common use case in Amazon Machine Learning business problems.

How to Answer

Discuss the key differences in how these algorithms handle data, build models, and their typical use cases.


“XGBoost, a gradient boosting method, builds trees sequentially, with each tree correcting the errors of the previous ones. It’s efficient in cases of high bias, like underfitting. In contrast, Random Forest builds trees in parallel and averages their predictions, making it effective for reducing variance and overfitting. For instance, in a scenario with noisy Amazon customer review data, I’d lean towards Random Forest for its robustness to overfitting.”

11. How would you analyze a dataset where you are informed that the data is noisy, volatile, and may not be fully accurate?

Demonstrating your ability to analyze noisy and potentially inaccurate data is critical as it tests your experience in handling real-world data challenges.

How to Answer

Emphasize the importance of data cleaning, outlier detection, and smoothing techniques.


“I would first clarify how the data is being used and if this noise is unusual in the specific business case. For a volatile dataset, I’d first perform EDA and identify outliers using IQR and z-score methods. I would confer with domain experts to check if potentially suspect values are feasible or not. Additionally, I’d explore data smoothing techniques and anomaly detection methods to manage volatility and inaccuracies in the dataset, ensuring the analysis results are reliable and meaningful.”

12. If two features are highly correlated in a random forest, how will both those features appear in a measurement of feature importance?

Understanding the impact of feature correlation is crucial to gauging your depth in model interpretation.

How to Answer

Discuss how feature importance in random forest might be affected by correlated features and the potential misleading interpretations that might arise.


“Feature importance is measured using a technique called permutation importance. Permutation importance is a way of measuring the contribution of each feature to the model’s prediction accuracy by randomly permuting the values of that feature and measuring the resulting decrease in accuracy. If two features are highly correlated, they may both have high permutation importance scores, because they are both contributing to the model’s ability to make accurate predictions. However, the specific way in which they are combined in the model can affect how they appear in a measurement of feature importance.”

13. What does it mean to use “inception architecture” for a convolutional neural network?

In an Amazon ML interview, discussing the “inception architecture” in CNNs shows your familiarity with advanced neural network architectures and their practical applications.

How to Answer

Explain the inception architecture concept, focusing on its unique structure and advantages, especially for complex image processing tasks.


“Inception architecture in a convolutional neural network refers to a network design where multiple filter sizes are applied simultaneously to the same input layer. This allows the network to capture information at various scales and complexities. It’s particularly useful in scenarios where the scale of features in images varies significantly. This architecture can enhance the performance of image classification tasks at Amazon, given the diverse range of product images.”

14. When designing neural networks for image classification, how does the Adam optimization algorithm differ in the way it works from other optimization methods?

This interview question tests your knowledge of optimization algorithms, as this is key in enhancing the performance of neural network-based classification problems.

How to Answer

Explain Adam’s unique features compared to other optimizers and why it can be more effective for certain tasks.


“Adam optimization differs as it combines the benefits of two other extensions of stochastic gradient descent – AdaGrad and RMSProp. It computes adaptive learning rates for each parameter. In image classification, Adam’s benefits include handling sparse gradients and non-stationary objectives effectively, making it suitable for large datasets with complex architectures often encountered at Amazon. Its ability to quickly converge and its efficiency in memory usage are significant advantages over traditional optimization methods.”

15. Let’s say you are developing a spam classifier to classify emails. You try several different classifiers like SVM, Random Forests, etc., but none of them produce satisfactory results. So, you decide to combine them using stacking. What classifier should you use as the meta-classifier in your stacking model?

Amazon will require you to understand these concepts for common use cases such as fraud detection or customer review analysis.

How to Answer

Discuss the rationale behind selecting a meta-classifier, considering its role in refining the predictions of the base classifiers.


“In the stacking model for a spam classifier, a good choice for a meta-classifier could be a logistic regression model. It’s effective in combining the predictions as it can interpret the outputs of the base classifiers (like SVM and Random Forests) as features and produce a final classification. Logistic regression is simple yet powerful for binary classification tasks like spam detection, making it a suitable meta-classifier to refine and improve upon the individual predictions of the base models. In complex scenarios or with more intricate data, Amazon might consider alternative options like neural networks or decision trees, which can capture more complex relationships between the base classifiers’ outputs.”

16. Why do you want to work with us?

The interviewer wants to know that you aren’t applying randomly and have an active interest in working for Amazon. Check their About page for more information on their guiding values and goals.

How to Answer

Start with what you admire about Amazon and how it ties with your mission and career goals. Demonstrate that you know the company, position, and the work that the team does. Focus on Amazon’s role as a leader in innovation, its commitment to customer-centric solutions, and the opportunity to work on challenging problems at scale.


“I want to work at Amazon because its business strategy of implementing AI and machine learning to solve problems at scale is a challenge that deeply motivates me. The opportunity to contribute to projects that impact millions of customers globally promises a great learning experience. I’m also drawn to Amazon’s culture of innovation and customer obsession, which aligns with my professional values and desire for impactful work.”

17. How do you prioritize multiple deadlines?

This question is asked to test your understanding of Amazon Leadership Principles like “Ownership” and “Bias for Action”. Prioritizing tasks effectively is crucial in fast-paced environments like Amazon, where you will face multiple competing deadlines.

How to Answer

When discussing prioritizing multiple deadlines, focus on your time management, ability to assess task importance, and strategies for planning and communicating. Provide examples from past successful projects.


“I would prioritize deadlines by assessing the impact of each task in line with the ‘Ownership’ principle. This means considering customer impact and long-term value. I would also employ ‘Bias for Action’ by making quick decisions on prioritization, ensuring efficient progress without unnecessary delays. Regular communication with stakeholders to align priorities and expectations would also be a key part of my approach.”

Tip: Practice more behavioral questions that test your ability to follow Amazon’s Leadership Principles. Keep a note of your past work and highlights, and find ways to quantify your impact wherever applicable. Familiarize yourself with the STAR (Situation, Task, Action, Result) method to structure your responses in a compelling and organized manner.

How to Prepare for a Machine Learning Interview at Amazon

Here are some tips to help you excel in your Amazon interview.

Tailor Your Resume

Have a clear understanding of the job description, and prepare your resume accordingly. The resume screening is what determines whether you’ll make it to the interview process; so make sure to highlight your work experience and skills in line with what the recruiter would want to see.

Study the Company and Role

Understand the specific ML applications and technologies Amazon uses.

Research recent news, updates, Amazon values, and business challenges the company is facing. Understanding the company’s culture and strategic goals will allow you to not only present yourself better but also understand if they are a good fit for you. You can also read Interview Query members’ experiences on our discussion board for insider tips and first-hand information.

Understand the Fundamentals

This interview will be an in-depth examination of your acumen in machine learning and programming. Be clear on core machine learning algorithms, data structures, and their applications, especially in the context of Amazon’s business use cases. Stay abreast of recent trends and news in ML and AI.

For more practice, refer to our handy guide on popular machine learning projects, or test your ML knowledge on our compilation of computer vision interview questions.

Highlight Your Soft Skills

Soft skills such as collaboration, effective communication, and flexibility are paramount to succeeding in any job, especially in a dynamic work environment such as Amazon.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.


What is the average salary for a Machine Learning Engineer role at Amazon?


Average Base Salary


Average Total Compensation

Min: $84K
Max: $192K
Base Salary
Median: $150K
Mean (Average): $147K
Data points: 205
Min: $9K
Max: $448K
Total Compensation
Median: $194K
Mean (Average): $202K
Data points: 202

View the full Machine Learning Engineer at Amazon salary guide

The average base salary for a Machine Learning Engineer at Amazon is US$146,766, while the estimated average total compensation is US$202,028, making the remuneration highly attractive for prospective applicants.

Check our main machine learning engineer salary page if you want to learn more about this in greater detail.

Which other companies can I apply to as a machine learning engineer aside from Amazon?

We highly recommend that you consider Netflix, Walmart, and Target. If you want to know how to succeed in interviews with any of these respected companies, please refer to our main company interview guide to learn more.

Are there job postings for Amazon Machine Learning roles on Interview Query?

You can visit our job portal and apply for your desired role, selecting by team, location preference, and your current skillsets. You should apply even when you do not possess 100% of the required skills, as many can be learned on the job. Have confidence and brush up on your fundamentals, and you should be good to go.


In conclusion, succeeding in an Amazon machine learning interview requires not only a strong foundation in ML algorithms but also the ability to apply them to real-world problems.

Understanding Amazon’s innovation-driven culture and preparing thoroughly with both technical and behavioral questions will be key to succeeding.

If you want to explore more content related to Amazon, check out our main Amazon guide. We’ve also covered other positions such as business analyst, data analyst, engineer, scientist, and more.

We wish you the very best in your journey to landing a fulfilling role at Amazon.