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The Amazon Machine Learning Engineer Interview

Understanding Amazon's culture, hiring process, and interview questions

Photo by Hello I'm Nik 🎞 on Unsplash
Photo by Hello I’m Nik 🎞 on Unsplash

Table of Contents

  1. Introduction
  2. The Machine Learning Engineer Role
  3. The Interview Process

Introduction

What started as an online book store is now a one trillion dollar e-commerce giant. Amazon is arguably one of the largest e-commerce giants in the world, and at the same time, it also focuses on cloud computing, digital streaming, and Artificial Intelligence.

It’s no surprise that one of the largest tech companies in the world is also a leader in integrating machine learning and artificial intelligence applications to improve the company’s operations and offerings. In fact, it’s because of machine learning that Amazon can sell more than twelve million products to over 100 million Amazon Prime subscribers and deliver them in one to two days.

In this article, we’ll take a closer look into what the machine learning engineer interview is like.


The Machine Learning Engineer Role

The Machine Learning Engineer at Amazon works with massive amounts of data to build Machine Learning (ML)and Deep Learning (DL) Models for various clients. He/she will work with Amazon’s Professional Services consultants, deliver ML/DL projects from beginning to end and help to operationalize models afterwards.

Required Skills and Qualifications

BASIC QUALIFICATIONS

  • B.S. degree in mathematics, statistics, computer science or a similar quantitative field
  • 5+ years of work experience in relevant field
  • Experience working with a wide range of predictive and decision models and data mining techniques, as well as tools for developing such models

PREFERRED QUALIFICATIONS

  • Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
  • Experience using Linux/UNIX to process large data sets
  • Experience with AWS technologies like Redshift, S3, EC2, Data Pipeline, & EMR
  • Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization

What are the types of machine learning roles?

At Amazon, there are multiple machine learning and Data Science roles that span across the organization. These roles include data scientists, applied scientists, machine learning engineers, and research scientists. While they are similar in nature, they have unique differences.

Data scientists at Amazon have a focus on providing data-driven insights and are the link between the business and the technical side. They are responsible for analyzing large data sets and modeling them as well.

Machine Learning engineers at Amazon are experts at building machine learning and deep learning models. They build models not only for Amazon but also for other large enterprises on the AWS. In addition to building models, machine learning engineers at Amazon are also responsible for implementing models and getting them ready for production.

Research scientists at Amazon typically have a higher level of education, usually a Master’s or a PhD. Research scientists are expected to push the envelope, meaning to extend the limits of what is possible. Research scientists will conduct research on old and new technologies to determine whether they’re beneficial in practice.

Applied scientists also usually have a higher level of education. It is a slightly higher role than a research scientist at Amazon and requires passing a coding bar. Applied Scientists focus on projects to enhance Amazon’s customer experience like Amazon’s Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Audio Signal Processing, text-to-speech (TTS), and Dialog Management.


The Interview Process

Initial Screen

The first phone screen is conducted by a recruiter or the hiring manager and is simply conducted to get a better understanding of each other. The interviewer will typically give you a walkthrough of the role and the interview process, and they’ll also ask you standard questions about your resume and past experiences. They want to see that you have an interest in Amazon and that you have the experience and competency that’s required for the role.

Technical Screen

After the initial phone screen is a technical screen, usually with a machine learning engineering manager. In this time, they’ll ask you a series of general questions on machine learning concepts. The questions are generally on fundamental machine learning concepts, like explanations of different machine learning models, bias-variance tradeoff, and overfitting.

The second part of the technical screen will be a coding question. You can use whatever language you prefer.

Check out the example questions and solutions from Amazon’s machine learning interview.


Onsite Interview

Lastly is the onsite interview, which usually consists of five to six rounds of interviews. These interviews are composed of a mixture of behavioral and technical interview questions.

Behavioral Questions: You can expect a behavioral question in every round and questions will cover things like your past Work experiences, why you’re leaving your current job, and how you work in teams. You should also expect them to ask an LP question (leadership principle) – make sure you know Amazon’s 14 leadership principles!

Technical questions: You should expect at least a couple of technical rounds that cover both machine learning concepts and programming concepts. Past interviewees were known to be asked object-oriented design questions, so make sure you brush up in both areas!

Here are some Amazon machine learning interview questions and solutions on Interview Query.

Thanks for Reading!


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