The demand for machine learning engineers has seen a significant uptick in the last few years. Big tech companies, including Amazon, Google, Facebook, and Microsoft (among others), have increased hiring for ML engineers to develop deep learning and AI algorithms to automate predictive models.
At Amazon, the machine learning engineers are experts at building ML and deep learning models. They build these models not only for Amazon but for other large enterprises on AWS.
Machine learning engineers at Amazon have interesting workdays, balancing their time between research, learning, prototyping, and delivering results. If you are preparing for Amazon’s machine learning interview, this article will give you an idea of the interview process, what Amazon looks for in ML engineers and the things you should do to crack ML interviews.
Here’s what you can expect from this article:
- Amazon Machine Learning Engineer Interview Process
- Typical Responsibilities of ML Engineers at Amazon
- Qualifications for Amazon ML Engineers
- Amazon Machine Learning Engineer Interview Questions
- Tips to Crack the Amazon ML Interview
- Gear Up for Your Next ML Interview
Amazon Machine Learning Engineer Interview Process
The machine learning interview process at Amazon is very similar to the interview process for software engineers. The only difference is that ML interviews have a dedicated round for ML and AI questions and don’t have a systems design round.
Here’s what the interview process for ML engineers looks like:
Initial Phone Screen with a Recruiter
This is the initial round where a recruiter will contact you to ask you a few basic questions about your experience and ML skills. If your skills are in sync with the role’s responsibilities, your next round will be scheduled.
Recommended reading: How to Prepare for and Crack Phone Screen Interviews at FAANG
Technical Phone Screen
A hiring manager from Amazon’s ML team will interview you in this round. You’ll be asked to solve a coding problem on core data structures to evaluate your approach and ability to solve problems by applying multiple programming concepts.
You’ll also be asked a few theoretical machine learning questions. If you meet the expectations in this round, your on-site interview will be scheduled in a week or two following your technical phone screen.
Recommended reading: Amazon Phone Interview Questions
Amazon’s onsite interview is also called The Loop. During the onsite, you go through a series of interviews (of around 3-4 rounds) that test you on a variety of areas. Here are the rounds that take place during the onsite interview:
- The Coding Round: You’ll be asked to solve a coding problem on algorithms in this round. Your knowledge of graph algorithms, greedy algorithms, Trees, and Dynamic programming is tested during this round.
- The ML Round: This round will involve answering a number of questions on ML concepts. You’ll also be asked to share your ideas for an ML system for predictive data models.
- Behavioral Round: The behavioral interview at Amazon revolves around the company’s 14 leadership principles. Your approach to answering questions and the clarity of your answers will give you points in this round. You’re also asked questions on workplace situations and relationships with coworkers/superiors.
- The Bar-Raiser Round: This round is specific to Amazon, where a Bar-raiser will evaluate if you’re the right cultural fit at Amazon. Bar raisers are specially trained employees who ensure that the hiring bar is maintained at a high level. You’ll mostly be asked some questions about Amazon’s leadership principles and workplace situations.
Recommended reading: How to Prepare for the Amazon Onsite Interview in 2 Months
Typical Role of Machine Learning Engineers at Amazon
Here’s what your role as a machine learning engineer at Amazon would involve:
- Develop deep learning and machine learning algorithms for predictive models
- Collaborate with the software engineering and data engineering teams to develop predictive data models
- Design and develop scalable machine learning and AI systems
- Integrate ML and AI into business applications to automate a whole range of processes
- Designing and developing the architecture for data models and taking data-driven decisions by interpreting model metrics
Qualifications Required to be a Machine Learning Engineer at Amazon
The below qualifications are what Amazon looks for in engineers applying to ML roles:
- While a bachelor’s degree in computer science is good enough to apply to ML jobs at Amazon, a master’s degree in Computer Science, IT, or a related field is preferred.
- Familiarity and proven working knowledge of ML concepts, including data governance, application development, and infrastructure development.
- Proven working knowledge of building and designing algorithms for data models.
- Proven working knowledge of an object-oriented programming language, preferably Java, Python, or C++.
- 5+ years of experience in the field of machine learning.
Amazon Machine Learning Engineer Interview Questions
The interview questions asked at Amazon’s ML interview can be classified into three main categories.
These questions revolve around core data structures and algorithms. Below are the topics to prepare and some sample coding interview questions. For more practice questions, check out Amazon Coding Interview Questions and our exhaustive Problems Page
Topics to prepare for the Amazon coding interview:
- Given an integer array arr of size n, find all magic triplets in it. A Magic triplet is a group of three numbers whose sum is zero. (Solution)
- Given a stream of integers, find the median of the set of integers read from the stream so far. If the median is a non-integer, round it down to the nearest integer (Solution).
- Given an array of time intervals (in any order) inputArray, of size n, merge all overlapping intervals into one and return the resulting array outputArray, such that no two intervals in output Array are overlapping. (Solution)
- Given two arrays: 1) arr1 of size n, which contains n positive integers sorted in the ascending order. 2) arr2 of size (2*n) (twice the size of first), which contains n positive integers sorted in the ascending order in its first half. Second half of this array arr2 is empty. (Empty elements are marked by 0). Write a function that takes these two arrays, and merges the first one into the second one, resulting in an increasingly sorted array of (2*n) positive integers. (Solution)
- Inorder traversal - Process all nodes of a binary tree by recursively processing the left subtree, then processing the root, and finally the right subtree. Preorder traversal - Process all nodes of a binary tree by recursively processing the root, then processing the left subtree, and finally the right subtree. Given the inorder and preorder traversal of a valid binary tree, you have to construct the binary tree. (Solution)
Want access to more coding problems along with complete solutions? Visit the Problems page.
Machine Learning Interview Topics and Questions
Topics to prepare for the Amazon ML interview:
- Cloud Vision
- Model Validation
- Model Optimization
- Deep Learning Models
- Predictive Models
- Data Processing
- ML and DL frameworks
- Amazon ML engine
- Explain CCA and ICA. How do you get a CCA objective function from PCA?
- Explain the process of finding thresholds for a classifier.
- Explain your idea to build a booking model to predict prices for accommodations on Airbnb.
- Which model among Random Forest Regression and Linear Regression would you prefer, and why?
- Explain the difference between Logistics Regression and Support Vector Machines.
Behavioral Interview Questions
Behavioral questions are an important part of the decision-making process at Amazon. These sample questions will give you an idea of the type of questions to expect at Amazon’s machine learning interview.
- How do you make sure to avoid burnout when you’re working on a challenging project?
- Have you had to adapt quickly when you started out on a new project? Give us an instance.
- Tell us about a time when you disagreed with your superior?
- Tell us about a time when you missed a project deadline? How did you deal with the situation?
- Tell us about a time when you had to deal with a difficult client.
Check out a long list of Amazon Behavioral Questions and Amazon Leadership Principles Questions specially curated for Amazon’s behavioral interviews.
Tips to Crack the Amazon Machine Learning Interview
Below are some quick tips to crack the machine learning interview at Amazon:
- Begin your prep at least 10 weeks in advance, mostly because the extent of topics to cover are significantly vast.
- Practice whiteboard coding for the onsite interview, as recruiters can ask you to write code on a whiteboard. If you don’t have prior practice, it can be extremely difficult to bring your thoughts together and write error-free code on a whiteboard.
- Amazon lays enormous emphasis on behavioral interviews. The bar-raiser round is designed specifically for that - to evaluate if you’re the right cultural fit at Amazon. That said, do not ignore preparing for behavioral interviews.
- Structure your answers to questions about past projects in the STAR format. This way, you give recruiters a clear idea of what went down.
- Create a portfolio of your past projects and list details in the STAR format. Having a ready portfolio can put you miles ahead of the competition.
- While practicing problems, make sure you identify inherent solution patterns and apply them to new problems. This is the only proven way to amp up your problem-solving skills.
- Become well-versed with not just ML concepts but also core concepts of the programming language of your choice.
If you’re a software engineer, software developer, engineering manager, or tech lead preparing for tech interviews, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready!
Also, read How hard it is to get a job at Amazon and How to get Software Engineering jobs at Amazon for specific insights and guidance on Amazon tech interviews.
Gear Up For Your Amazon ML Interview
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