Amazon Machine Learning Engineer (MLE) Interview Guide

Are you interested in how machine learning (ML) and AI can transform global business practices? Passionate about building, training, and deploying ML models for any use case? If so, the Amazon machine learning engineer role might be a great fit for you.

The multifaceted tech giant Amazon incorporates generative AI throughout its diverse product ecosystems, which couldn’t be accomplished without the hard work of Amazon machine learning engineers. In this guide, we’ll explore Amazon’s AI/ML opportunities, their unique interview loop, and tips to strengthen your application for this highly competitive role.

What does an Amazon MLE do?

Machine learning happens at every level of design, business, and customer service at Amazon, so MLEs work on a variety of teams and projects. That includes projects related to pricing, customer operations, and even Prime Video.

However, much of Amazon’s MLE work takes place in the Amazon Web Services (AWS) ecosystem. AWS is Amazon’s cloud computing network that includes complex systems of cloud resources, ML models, database management, virtual private server content, and more. Given its wide scope of projects in processing and using data, AWS often hires new MLEs and applied scientists.

The day-to-day activities of a machine learning engineer depend on which of the teams you apply for. Regardless of specifics, Amazon is looking for MLEs who embody a combination of technical skills and an understanding of the leadership principles behind Amazon’s business practices. Whether developing algorithms for business applications, building ML models, or otherwise, Amazon is dedicated to cultivating a customer-obsessed attitude.

What are the typical job requirements for an Amazon MLE?

Education: Although there are exceptions, most machine learning roles at Amazon require a Master’s Degree or PhD. For many roles, PhDs are included in the basic qualifications, particularly in specializations such as:

• Machine Learning/Deep Learning
• Algorithms
• Mathematics
• Computer Vision

While roles for candidates with less education experience exist, most machine learning roles require strong educational and professional experience to build the future of Amazon’s technical product space.

Before applying to a role, make sure you’re prepared to be assessed in all software, toolkits, frameworks, and/or coding languages listed as required in the job posting. Amazon often tests candidates before moving them forward in the interview process.

Experience: Amazon is a great early-mid career opportunity for ML/AI experts, as most of these roles require 3-5+ years of field experience depending on the level of seniority. Since every aspect of work at Amazon is highly collaborative, a strong history of working in team environments is a must.

Although MLE roles are individual contributor technical roles, Amazon seeks ML candidates with excellent written and verbal communication skills, as well as strong publication records in top journals/conferences. Candidates should go above and beyond to show their passion for and dedication to the future of AI.

Since every role has unique requirements, we’ve included a few examples of various MLE postings below. Here are the requirements for an MLE on a team building LLM-based services with a focus on enhancing developer experience in the Cloud:

Required:

• PhD with relevant machine learning specialization
• Experience with deep learning frameworks (TensorFlow, PyTorch, MXNet) and related libraries
• 3+ years of professional experience
• Ability to thrive in a team environment
• Excellent written and verbal communication skills
• Strong publication record at top conferences (NeurIPS, ICML, ICLR, AISTATS) or journals like JMLR

Preferred:

• Experience in AutoML: hyperparameter optimization, meta learning, and automated decision making
• Skilled in modeling natural text with transformer networks, specifically large language models
• 5+ years of professional experience

Here are the requirements for an MLE working on the Amazon transportation systems (ATS) team:

Required:

• Experience building ML models and developing business algorithms
• PhD or Master’s Degree in a relevant field
• 3+ years of CS, CE, and/or ML experience
• Has patents or publications at top-tier conferences or journals
• Programming skills in Java, C++, Python, or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred:

• Competency in Unix/Linus
• Any number of years of experience in professional software development

Recommendations before you apply for Amazon MLE roles

• Don’t judge a job by its title. A lot of job titles fall under the “machine learning” umbrella at Amazon: applied specialist, data scientist, solutions architect, machine learning engineer, AI/ML specialist, and software development engineer, to name a few. This is due to the size of the company and the way that different teams might choose to label their needs. Don’t let a job title you haven’t heard of before deter you from applying. If you’re a good fit based on qualifications, apply!
• Pick the right role. Along the same lines, make sure the role(s) you’re applying for fits your resume and qualifications best out of what Amazon has listed. Likewise, don’t flood recruiters with multiple applications. Pick the 1-3 jobs that you’re most qualified for, and focus on creating strong pitches for those applications as opposed to applying broadly within the company.
• Simplify your resume. While we’re not saying to cut things out of your resume, it’s important to create a streamlined experience for recruiters. Consider creating a custom resume with keywords from the job posting, making sure your useful qualifications are quick and easy to find, and highlighting your most relevant career experience. This will help make your resume stand out.

Interview Process

Amazon’s MLE interview loop features a preliminary assessment that depending on your prospective role, might include an online skills test and/or a recruiter screen, followed by three or four 55-minute interviews covering:

• System design
• Coding
• Leadership

Unlike some other company interview loops, Amazon does not separate question types into specific interviews. Candidates can expect to have 1 system design interview and 2-3 coding interviews with leadership, or behavioral, questions throughout.

The preliminary assessments are always virtual, but depending on your prospective role and location, your full loop could be on-site or remote (generally hosted on Amazon Chime). Your recruiter will share more information as it is relevant.

Assessment/Recruiter Screening

The first stage of the Amazon interview will vary based on your background. If you were recruited from a university, you might skip this round altogether. However, online applicants to a technical role can expect to receive a coding and system design challenge.

For coding, you can expect a 90-minute exercise that involves two questions. Amazon provides a sample test for candidates to attempt online before applying. It’s a strong choice to solve the coding problem in a programming language listed on the job posting, but Amazon is open to many different languages as long as you find the needed solution. The possible programming languages you are allowed to use in Amazon challenges include:

• C
• C++
• C++14
• C#
• Go
• Java7
• Java8
• JavaScript
• Kotlin
• Objective-C
• PyPy2
• PyPy3
• Python2
• Python3
• Ruby
• Scala
• Swift

The system design aspect of the assessment is much more dependent on your prospective role. Prepare to answer questions related to a few fictional situations with simulated peers, managers, and stakeholders.

Additionally, you may have a short, 30-minute phone screening with a recruiter. Be prepared for standard behavioral questions—which we cover in more depth later in this guide—and to express your interest in working at Amazon in particular. Brushing up on Amazon’s machine learning research and innovation is a great way to prepare for your recruiter chat.

If you pass the assessment and preliminary screen, you’ll move on to the full interview loop. Below are notes on the three primary types of interview questions that’ll be asked.

System Design

A comprehensive understanding of good design is essential to creating strong code. Amazon seeks candidates for their ML ecosystem who understand how to create effective system design with a focus on software development. MLEs at Amazon must be able to analyze, communicate, and ideate on the systems that make Amazon run.

With these questions, Amazon is looking for:

• Ability to design long-lasting, maintainable software
• Communication skills for stakeholders of a variety of technical levels
• Assessment of system design in terms of deployment, scaling, failures, availability, and performance
• Discussion of latency and concurrency

Expect to see system design questions linked to coding questions and use this as an opportunity to display your multi-faceted approach to problem-solving. Amazon wants MLEs who can develop from a design perspective. Before you tackle any of these questions, remember to ask clarifying questions if necessary, fulfill question requirements, design for performance, and always identify shortcomings when you see them.

Here are some system design questions you can expect:

Coding

Given the nature of Amazon’s MLE roles, it’s crucial that you demonstrate your programming competence during the MLE interview. Before interviewing, brush up on the programming language(s) listed on the job listing.

Amazon seeks MLEs whose work is:

• Logical and Maintainable, or the ability to write maintainable, readable, reusable, and understandable code
• Structured in Data and Algorithms to write code in the appropriate data structure to solve the problems you’re given
• Focused on Problem-solving by taking something complex, breaking it down, identifying the solution, and translating that into working code

Each interview focuses on one of these aspects of code alongside behavioral questions. Be prepared to solve problems in a timely manner, and ask clarifying questions if you need to. While providing a correct solution is very important, you can also go a step further by justifying decisions and considering different factors through clear communication.

Here are some general coding questions you can expect:

Check out Exponent’s extensive Coding Interview Practice and Leetcode 75 Essential and Trending Problems as a resource for improving your ability to solve coding questions and effectively articulate your process.

Leadership

Amazon values employees who can easily collaborate with others. While technical qualifications are important, soft skills are key to building the culture of curiosity, resourcefulness, and innovation that Amazon aspires to create company-wide. Expect to receive behavioral questions throughout all aspects of the interview loop, and never be afraid to share a personal anecdote as part of a technical answer.

With these questions, Amazon is looking for:

• Excellent communication skills
• Dedication to servant leadership even in individual contributor roles
• Passion for your prospective work
• Display of Amazon’s Leadership Principles in action

As you prepare for the interview loop, remember that Amazon considers leadership skills so important that they incorporate behavioral questions into every interview. Don’t underestimate the value of answering these questions effectively, and be sure to work on your communication skills in addition to system design and coding.

Here are some behavioral questions you can expect:

Tips and Strategies

• Data, data, data. Although Amazon is interested in candidates with new and innovative ideas for the company, it’s critical to display your dedication to metrics-driven problem-solving. Regardless of question type, make sure your answers keep data in mind, even in hypothetical circumstances. This will demonstrate your reliability as a prospective team member and your effectiveness as a decision maker.
• Practice behavioral questions. Displaying leadership principles is a huge aspect of successfully interviewing at Amazon, even for individual contributor roles. Amazon seeks out candidates with strong behavioral skills to build their teams. Before interviewing, check out the company’s Leadership Principles Guide and practice articulating ways that you’ve led on previous teams. You don’t need to have held a managerial position before, but showing your ability to communicate, collaborate, and rise to challenges is key to a successful Amazon interview.
• Keep it concise. 55 minutes might seem like a lot of time, but it goes faster than you’d think. When answering questions, make sure to stay on task and not elongate your answer. This gives you more time for other questions and facilitates a more productive dialogue with the interviewer. Amazon suggests using the STAR Method when possible. It’s okay to ask for clarifying questions or have a follow-up statement, but try to stay on topic as much as you can.

Additional Resources

• ML Jobs at Amazon
• Complete Software Engineering Interview Course
• Interviewing at Amazon
• Amazon Software Design Engineer (ML ecosystem) and SDE II Interview Prep
• Machine Learning on AWS
• Amazon Interview Question Database

FAQs

• Does Amazon offer internships? Yes! Amazon has a large internship program with a focus on networking and mentorship for students. For ML/AI roles, options are generally limited to graduate and postgraduate candidates. It offers PhD internships in machine learning and applied science internships for graduate students.
• Can I interview again if I’m rejected? If you’re rejected due to a skills assessment, you must wait 90 days before reapplying. For all other rejections, you can reapply anytime and your eligibility will be assessed, although it’s generally considered best to wait six months or until you have made corrections or improvements to your application.
• What is Amazon’s mission? Amazon is guided by four principles that drive every aspect of the company: customer obsession rather than competitor focus; passion for invention; commitment to operational excellence; long-term thinking. These align with Amazon’s long-term mission to be “Earth’s most customer-centric company, Earth’s best employer, and Earth’s safest place to work.” While these concepts are much larger-scale than any individual employee or job application, keeping these in mind throughout your interview process can show a passion for and alignment with Amazon’s company goals.