Amazon Machine Learning Engineer Interview Guide
Detailed, specific guidance on the Amazon Machine Learning Engineer interview process - with a breakdown of different stages and interview questions asked at each stage
The role of an Amazon Machine Learning Engineer
Some key responsibilities of an Amazon Machine Learning Engineer include working on projects such as natural language processing, computer vision, speech recognition, and recommender systems. Understanding of machine learning concepts, including deep learning, reinforcement learning, and supervised and unsupervised learning is a must. You would be expected to stay up-to-date with the latest advancements in machine learning technologies and apply them to Amazon's unique business needs.
In addition to technical skills, Amazon Machine Learning Engineers should have strong communication and collaboration skills to work effectively with cross-functional teams. They should be able to explain complex machine learning concepts to non-technical stakeholders and be able to provide recommendations on how machine learning can be applied to business problems.
Amazon offers several Machine Learning Engineer positions across different teams and business units. These positions involve working on a variety of projects, such as developing intelligent voice-based systems, building machine learning solutions for Amazon's cloud services, optimizing robot movement in warehouses, and developing ad targeting algorithms. Other teams that may hire Machine Learning Engineers include Amazon Prime, Amazon Music, and Amazon Devices.
These positions may vary depending on the business unit and location, but the primary focus is to leverage data to drive business results and improve customer experiences.
How to Apply for a Machine Learning Engineer Job at Amazon?
Check out Amazon’s career page and browse through the Machine Learning Engineer job listings. When you find a role that interests you, be sure to read through the job requirements and qualifications carefully to ensure you meet the criteria. If you have any connections within the company, consider reaching out to them for a referral as it highly increases your chance. When you apply, make sure to tailor your resume to align with the qualifications listed in the job posting. This will help you stand out from other applicants. And if you need help with customizing your resume specifically for Amazon (or for that matter, any other company), Prepfully provides resume review services by experienced recruiters in your target company that can give you feedback on your resume. It's worth noting that the application process may vary depending on the position and location, and the company may conduct additional assessments or interviews as part of the selection process.
Amazon Machine Learning Engineer Interview Guide
As a part of the Amazon Machine Learning Engineer interview, you will need to go through multiple interview rounds. The interview process and questions may differ for different positions and roles.
1. Recruiter Screen - The first round interview is an initial phone screen. The purpose of this round is to assess your fit for the role based on their behavioral competencies and fundamental understanding of machine learning concepts.
2. Technical Interview Rounds - The second round consists of multiple rounds, which can include one or more of the following: ML quiz, DSA coding and online assessments.
3. Onsite Interview Round - The final round is an Onsite behavioral round that assesses your alignment with Amazon's leadership principles.
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Amazon MLE Round 1: Recruiter Screen
Overview
The first round interview is an initial phone screen. The purpose of this round is to assess your fit for the role based on their behavioral competencies and fundamental understanding of machine learning concepts. During the interview, the Amazon recruiter or hiring manager will ask questions related to your experience with machine learning and their ability to apply machine learning to solve real-world problems. The interviewer may also inquire about your approach to staying up-to-date with the latest developments in the machine learning field and their ability to overcome technical challenges while working on a project.
Interview Questions
- Name a time you were innovative
- Name a time you delivered a simple solution to a complex problem.
- How to deal with a troublesome dataset.
- How to deal with misrepresentative training data? (imbalanced dataset, overfitting, explain how L1/L2 regularization work at an optimization level)
- How to deal with a large dataset where only a few examples are labeled?(semi-supervised learning)
Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview
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Amazon MLE Round 2: Technical Interview Rounds
Overview
The second round consists of multiple rounds, which can include one or more of the following:
- ML Quiz Round: Here you can expect to face deep questions related to machine learning concepts, such as support vector machines (SVM) and batched training. The interviewer will assess your proficiency in machine learning algorithms and their ability to apply these concepts to solve real-world problems.
- DSA Coding Rounds: In this round, you will be given coding challenges related to data structures and algorithms. The interviewer will assess your ability to write efficient and clean code, their understanding of data structures, and their ability to apply algorithms to solve complex problems.
- Online Assessments: In some cases, candidates may also be given online assessments after the recruiter screen as coding challenges. These assessments include multiple-choice questions or coding challenges, and they will assess your proficiency in programming languages, data structures, and algorithms.
Interview Questions
- What kind of different loss functions do you know?
- How do you measure performance of computer vision models?
- Describe normalization and bayes’ rule.
- Given preorder and inorder traversal of a tree, construct the binary tree.
- What is the K-means Algorithm?
- What's the difference between the summaries of a Logistic Regression and SVM?
- Describe linear regression vs. logistic regression.
- Explain advantages and drawbacks of SVM.
- Write a function to find the largest element in an array.
- Implement a stack data structure in Python.
- Given an array of integers, find the two elements that add up to a given sum.
- Implement a function that returns the nth Fibonacci number using recursion.
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Amazon MLE Round 3: Onsite Interview Round
Overview
The final round is an Onsite behavioral round that assesses your alignment with Amazon's leadership principles. The interviewer will ask questions related to your past experiences and how they handled specific situations, assessing their ability to think critically, innovate, and demonstrate customer obsession.
Amazon has 14 leadership principles that they use to guide their business practices and evaluate their employees. Some of these principles include customer obsession, ownership, bias for action, and earning trust. During the interview, the interviewer will assess how well you align with these principles and their ability to apply these principles to their work.
Interview Questions
- How do you handle situations where you have to make a decision with limited information?
- Can you give an example of how you have taken ownership of a project or problem, and what was the outcome?
- How do you balance long-term strategic planning with short-term goals?
- Can you describe a time when you identified a problem and came up with a creative solution to solve it?
- How do you ensure that your work meets high quality standards?
Tips to stand out in the Amazon MLE Interview
When you are preparing for a Amazon Machine Learning Engineer Interview - we’d recommend keeping these things in mind:
- Research the company and the role thoroughly before the interview. Look up the leadership principles and try to align your experiences with them. Understand the requirements of the role and the technologies used in the company. We’d recommend checking out Working at Amazon for more information.
- Prepare for the technical rounds by practicing coding problems and data structures. Brush up your knowledge of machine learning algorithms and concepts.
- During the recruiter screen and onsite round, focus on demonstrating your behavioral competencies, such as leadership, teamwork, and problem-solving skills. Prepare examples of how you have applied these competencies in your past experiences.
- During the interview, try to communicate your thought process clearly and concisely. Explain your approach to solving a problem and justify your decisions.
- Be confident and enthusiastic. Amazon values innovation and a passion for learning. Show your enthusiasm for the role and your willingness to learn and grow.
- At the end of the interview, ask relevant questions about the company, the team, and the role. It demonstrates your interest and curiosity about the job.
Responsibilities of a Machine Learning Engineer at Amazon
The responsibilities of a Machine Learning Engineer at Amazon across roles can broadly be seen as-
- Propose, design, and implement high-performance ML platform solutions that significantly advance the deployment of models that serve millions of customers.
- Design, develop, and deploy complex machine learning models and algorithms to solve business problems. For instance, build a machine learning model to detect fraudulent transactions on Amazon's payment platform.
- Collaborate with cross-functional teams of software developers, data scientists, and product managers to define requirements, develop solutions, and deploy models at scale.
- Stay up-to-date with the latest advancements in machine learning technologies and apply them to Amazon's unique business needs.
- Work on projects such as natural language processing, computer vision, speech recognition, and recommender systems. For instance, develop a natural language processing model to analyze customer feedback and improve product recommendations on Amazon's website.
- Have a strong understanding of machine learning concepts, including deep learning, reinforcement learning, and supervised and unsupervised learning.
- Proficient in programming languages such as Python, Java, and C++, as well as have experience with popular machine learning frameworks like TensorFlow and PyTorch.
Skills and Qualifications needed for Machine Learning Engineers at Amazon
Here are some skills and qualifications that will help you excel in your Machine Learning Engineer interviews at Amazon. One thing to note here is that the degree qualification is different for every role.
- It's beneficial to have at least 5+ years of experience in DS, SE or MLE roles
- Understand the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience.
- Experience leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience. Brush up your programming skills with at least one software programming language.
- Develop strong interpersonal skills and the ability to cultivate relationships with multiple collaborators. This will allow you to successfully partner with others and drive results.
It's important to keep in mind that this list is not exhaustive, and the requirements and qualifications may vary depending on the position and location. It's always best to check the job description and requirements on the Amazon's Career page before you apply for the role.
Salary Ranges
The average salary for a Machine Learning Engineer at Amazon is around $150,000 per year, with a range of $120,000 to $190,000 per year depending on experience and other factors. It's important to note that salaries can vary based on a number of factors such as location, specific job responsibilities, and level of experience.
Conclusion
The interview process for a Machine Learning Engineer role at Amazon typically includes 3 primary rounds - a recruiter screen, followed by technical interview rounds, and the final onsite interview round to conclude. The first round interview is an initial phone screen. The purpose of this round is to assess your fit for the role based on their behavioral competencies and fundamental understanding of machine learning concepts. The second primary round consists of multiple rounds, which can include one or more of the following: ML quiz, DSA coding and online assessments. The final primary round is an Onsite behavioral round that assesses your alignment with Amazon's leadership principles.