Amazon Machine Learning Engineer Interview (questions, process, prep)

Machine learning engineer interviews at Amazon are really challenging. The questions are difficult and specific to Amazon and cover a range of topics.

The good news is that the right preparation can make a big difference and can help you land an MLE job at Amazon. We have put together the ultimate guide below, to help you maximize your chances of success. Special thanks to our expert coaches Sandeep, Bilwasiva, and Dessy for their insights.

Here's an overview of what we'll cover:

• Role and salary
• Process and timeline
• Example questions
  • Coding interview
  • Machine learning fundamentals
  • Machine learning system design interview
  • Behavioral interview
• Interviewing tips
• Preparation plan

Click here to practice 1-on-1 with ML ex-interviewers

1. Amazon Machine Learning Engineer Role and Salary↑

Before we get into your Amazon ML engineer interviews, let’s take a look at the role first.

1.1 What does an Amazon Machine Learning Engineer do?

An Amazon machine learning engineer’s responsibility is to develop and optimize machine learning models and algorithms, using these techniques to solve various problems and enhance processes across Amazon’s products, features, and services. 

According to Amazon interview coach Sandeep, Amazon’s ML engineers collaborate with various teams across the company to find practical ways to solve real-world problems. Together with ML scientists and other engineers, you could work on projects like demand forecasting, speech processing, robotics, firmware, and more. You could also work with big data to streamline sales processes, boost efficiency, and improve overall customer experiences. An example of this is building models for Amazon’s product search engine, contributing to its position as one of the world’s best.

Finally, machine learning engineers at Amazon help keep the company at the forefront of the ML industry at large. Along with ML scientists, Amazon ML engineers actively publish research papers, newsletters, and vlogs.

1.2 How much does an Amazon Machine Learning Engineer make?

Based on Glassdoor data, the median total pay for an Amazon machine learning engineer is $234k, 29% higher than the median total pay for ML engineers in the US.

Location is an important factor when it comes to salary. To compare, based on Glassdoor data:

• Amazon India ML engineer: est. average total pay $11k
• Amazon US ML engineer: est. average total pay $234k

Most ML engineering posts at Amazon are software development engineer posts. So to give you an idea of how much Amazon ML engineers make at the company, we’ve pulled the SDE info from Levels.fyi:

Ultimately, how you do in your interviews will help determine what you’ll be offered. That’s why hiring one of our Amazon ML interview coaches can provide such a significant return on investment.

And remember, compensation packages are always negotiable, even at Amazon. So, if you do get an offer, don’t be afraid to ask for more. If you need help negotiating, consider booking one of our salary negotiation coaches to get expert advice

2. Amazon Machine Learning Engineer Interview Process and Timeline↑

The Amazon interview process for the machine learning engineer role takes about four to eight weeks on average. Below we’ve outlined the steps you can expect, and how Amazon evaluates interviews and decides on a hire

2.1 What steps to expect

The interview process for the machine learning engineer role is similar to that of a software development engineer at Amazon. Here’s what you can expect:

• Resume screening
• HR recruiter email or call
• Online assessment
• Interview loop: 4 interviews

2.1.1 Resume screening

First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process—we’ve found that ~90% of candidates don’t make it past this stage.

So take extra care to tailor your resume to the specific position you're applying to.

If you’re looking for expert feedback, get input from our team of ex-FAANG/MAANG recruiters, who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.

2.1.2 HR recruiter email or call

Based on the SDE official interview guide, if you pass the resume screening, you should receive an email with a link to an online assessment, which we’ll cover below. However, we’ve seen reports of some candidates getting a call first instead of an email. During this call, the recruiter will discuss your interests to see what group or team would be best for you. They will also use this conversation to confirm that you've got a chance of getting the job at all.

You should be prepared to explain your background and why you’re a good fit for Amazon.

2.1.3 Amazon online assessments

After passing the resume screen and/or HR screen, you’ll receive a link to a self-administered online assessment (OA) via email with a 1-week expiration. 

Since the MLE roles on Amazon are SDE-MLE roles, our understanding is that it sends out the same OA for both roles:

Coding (90 minutes)

This part will require you to answer two technical questions. You can use any of these coding languages: C, C++, C++14, C#, Go, Java7, Java8, JavaScript, Kotlin, Objective-C, PyPy2, PyPy3, Python2, Python3, Ruby, Scala, and Swift.

According to this SDE-I candidate report, the coding OA may include 4 questions: 2 for coding, and another 2 for explanation. While this wasn’t mentioned in the official guide, it would be best to prepare for both scenarios. 

System design (20 minutes)

For this part, you don’t need to complete a whiteboarding or diagram exercise. For each system design scenario, you’ll be asked to rate actions from most effective or ineffective. 

Work style survey / Work sample simulation

After completing the system design module, you’ll be asked to fill in the work style survey, which will assess your work style via statements. This is heavily based on Amazon’s Leadership Principles. Expect 30 to 40 multiple-choice questions.

One interview candidate reports receiving a work sample simulation along with the work style survey. The simulation is a sort of “day in the life” type of activity. Your prompts may come in the form of emails, videos, or instant messages from a virtual manager or team member. You’ll also receive materials to inform your decision. You’ll be tested on your problem-solving skills in alignment with Amazon’s Leadership Principles. 

If you pass the online assessment, you can expect a 15-minute preparation session on Amazon Chime, the company's video conferencing product. Your recruiter will brief you on the rest of the interviews you can expect. 

2.1.4 Interview loop

After you pass the online assessments, the next step is the interview loop. For this round, you'll have a day packed with 4 interviews, which may be done virtually or in person at an Amazon office.

Each interview will last about 55 minutes and be one-on-one sessions with a mix of people from the team you’re applying to join, including peers, the hiring manager, and a senior executive. 

Here’s a sample interview schedule for the SDE position from Amazon’s interview prep guide to give you an idea of what the interview loop looks like:

 

Question types

One to two of your interviews will include coding questions (i.e. data structure and algorithm questions) which you'll need to solve on a whiteboard/online editor. One interview will cover machine learning fundamentals, and another for machine learning system design. You'll be asked behavioral questions in all your interviews.

All candidates are expected to do extremely well in coding and behavioral questions and demonstrate ML knowledge. If you're relatively junior (SDE-MLE II or below) then the bar will be lower in your system design interviews than for mid-level or senior engineers (e.g. SDE-MLE III or above).

One common mistake candidates make is to under-prepare for behavioral questions. Each interviewer is usually assigned two or three Leadership Principles to focus on during the interview. These questions are much more important at Amazon than they are at other big tech companies like Google or Meta.

Bar Raiser

Finally, one of your last interviews will be with what Amazon calls a “Bar Raiser”. These interviewers are not associated with the team you’re applying for, and focus more on overall candidate quality than specific team needs.

They get special training to make sure Amazon’s hiring standards stay high and don’t degrade over time, so they are a big barrier between you and the job offer.

Amazon doesn’t have an official interview prep guide from the machine learning engineer role. But you can still familiarize yourself with their engineering interview process by checking out Amazon’s official SDE interview process. 

2.2 What the Amazon interview evaluation form looks like

At the end of each interview, your interviewer will grade your performance using a standardized feedback form that summarizes the attributes Amazon looks for in a candidate. The form is constantly evolving, but we have listed some of its main components below.

A) Notes

The interviewer will file the notes they took during the interview. This usually includes the questions they asked, a summary of your answers, and any additional impressions they had (e.g. communicated ABC well, weak knowledge of XYZ, etc.).

B) Technical competencies

Your interviewer will then grade you on technical competencies. They will be trying to determine whether you are "raising the bar" or not for each competency they have tested.

In other words, you'll need to convince them that you are at least as good as or better than the average current Amazon MLE at the level you're applying for.

For coding, you’ll be evaluated on three competencies:

• Knowledge of data structures and algorithms
• Problem-solving skills
• Ability to produce logical and maintainable code

For the machine learning interviews, you’ll be assessed on the breadth of your ML knowledge and your ability to design ML systems.

C) Leadership Principles

Your interviewer will also grade you on Amazon's 16 Leadership Principles and assess whether you're "raising the bar" for those too. As mentioned above each interviewer is given two or three Leadership Principles to grill you on. We’ll cover these in detail in section 3.

D) Overall recommendation

Finally, each interviewer will file an overall recommendation into the system. The different options are along the lines of: "Strong hire", "Hire", "No hire", "Strong no hire".

2.3 What happens behind the scenes

Your recruiter is leading the process and taking you from one stage to the next. Here's what happens at each of the stages described above:

• After the online assessments, your recruiter decides to move you to the interview loop or not, depending on how well you've done 
• After the interview loop, each interviewer files their notes into the internal system, grades you and makes a hiring recommendation (i.e. "Strong hire", "Hire", "No hire", "Strong no hire")
• The "Debrief" brings all your interviewers together and is led by the Bar Raiser, who is usually the most experienced interviewer and is also not part of the hiring team. The Bar Raiser will try to guide the group toward a hiring decision. It's rare, but they can also veto hiring even if all other interviewers want to hire you.
• You get an offer. If everything goes well, the recruiter will then give you an offer, usually within a week of the onsite but it can sometimes take longer

It's also important to note that recruiters and people who refer you have little influence on the overall process. They can help you get an interview, but that's about it.

3. Amazon Machine Learning Engineer Example Questions↑

Okay, now that we've covered the interview process, let's dig into the four types of interviews that you'll encounter:

• Coding
• Machine learning fundamentals
• Machine learning system design
• Behavioral

Below, we've put together a summarized list of example questions for each of these interview types. We've also provided a few notes about them, which should help you get a better idea of what to expect.

3.1 Coding interview

Amazon’s machine learning engineers solve some of the most difficult problems the company faces with code. It's therefore essential that they have strong problem-solving skills. 

This is the part of the interview where you want to show that you think in a structured way and write code that's accurate, bug-free, and fast.

Here are the most common question types asked in Amazon coding interviews and their frequency. The percentages and examples come from Glassdoor data on Amazon software development engineers but should apply to machine learning engineer candidates as well.

1. Graphs / Trees (46% of questions, most frequent)
2. Arrays / Strings (38%)
3. Linked lists (10%)
4. Search / Sort (2%)
5. Stacks / Queues (2%)
6. Hash tables (2% of questions, least frequent)

Below, we've listed common examples used during Amazon SDE-MLE interviews for each of these different question types. To make these questions easier to study, we've modified the phrasing to match the closest problem on Leetcode or another resource, and we've linked to a free solution.

Finally, we recommend reading our guide on how to answer coding interview questions to understand more about the step-by-step approach you should use to solve these questions, as well as our list of 49 recent Amazon coding interview questions for more practice.

3.2 Machine learning fundamentals interview

Applied ML experience is not mandatory for all machine learning engineer roles at Amazon. However, the company does want its candidates to demonstrate a breadth of ML knowledge. It’s how they’ll assess whether you’ll be ready to work alongside seasoned ML practitioners. 

Most candidates refer to this interview as the ML quiz and may include questions ranging from basic to advanced, depending on the level you’re applying for. 

To help you prepare for it, we’ve gathered a few example questions from Amazon candidates, along with a few more from other machine learning engineer interviews reported on Glassdoor.

3.3 Machine learning system design interview

Amazon has huge data sets and billions of users across its diverse ecosystem. The magnitude and complexity of these systems present quite a few opportunities to apply machine learning to real-world problems. 

Broadly speaking, that's what you'll be asked to do in your interview: transform raw data, select the right model for the problem, monitor performance, etc.

The questions you'll be asked are similar to system design questions, in that you'll need to outline a high-level approach for a system or problem. The primary difference is that you'll be expected to specifically design a machine learning solution. 

To provide a clearer idea of what to expect, we've compiled a list of examples that are similar to the questions you'd be asked in Amazon’s machine learning system design interview from Glassdoor. Note that we've modified the phrasing in some cases to make the questions more clear.

3.4 Behavioral interview

Amazon’s interview process heavily focuses on assessing if you live and breathe the company’s 16 Leadership Principles. The main way Amazon tests this is with behavioral questions, which you'll be asked in every interview.

SDE interviews tend to primarily focus on the first four principles we have highlighted below, according to the Amazon ex-interviewers on our coaching team. Since most MLE roles on Amazon are considered SDE roles, it's our understanding that they’re the same in this respect. The other twelve topics also come up but less frequently.

Amazon's Leadership Principles:

• Customer Obsession
• Ownership
• Bias for Action
• Have Backbone; Disagree and Commit
• Invent and Simplify
• Dive Deep
• Are Right, A Lot
• Deliver Results
• Think Big
• Hire and Develop the Best
• Frugality
• Learn and Be Curious
• Insist on the Highest Standards
• Earn Trust
• Strive to be Earth's Best Employer
• Success and Scale Bring Broad Responsibility

Below is a breakdown of each leadership principle and how you’ll be asked about each during your interview process with Amazon.

3.4.1 "Customer obsession" interview questions

Customer obsession — "Leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. Although leaders pay attention to competitors, they obsess over customers.”

Customer obsession is about empathy. Interviewers want to see that you understand the consequences that every decision has on customer experience. You need to know who the customer is and their underlying needs, not just the tasks they want done.

This is by far the most important leadership principle used at Amazon. Therefore, it is the most critical one to prepare for.

According to Bilwasiva, Amazon interview coach, here are the best ways to answer ‘customer obsession’ questions: 

• Provide examples of how you've prioritized customer needs in your previous roles, showcasing your commitment to understanding and addressing customer pain points.
• Discuss specific initiatives or projects where you've gone above and beyond to deliver exceptional customer experiences, highlighting the outcomes and impact.

3.4.2 "Ownership" interview questions

Ownership — "Leaders are owners. They think long term and don’t sacrifice long-term value for short-term results. They act on behalf of the entire company, beyond just their own team. They never say “that’s not my job.”

Interviewers at Amazon want to avoid hiring people who think, “That’s not my job!” When answering ownership questions, you’ll want to prove that you take initiative, can make tough decisions, and take responsibility for your mistakes.

Dessy, ex-Amazon interviewer advises preparing a situation that shows how you take ownership of a project even when you work cross-functionally, and how you go above and beyond to follow through and deliver. 

3.4.3 "Bias for action" interview questions

Bias for action — "Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.”

Since Amazon likes to ship quickly, they also prefer to learn from doing (while also measuring results) vs. performing user research and making projections. They want to see that you can take calculated risks and move things forward.

Bilwasiva, Amazon interview coach advises preparing examples that “emphasize the importance of learning from failures and iterating on ideas to continuously improve and move forward.”

3.4.4 "Have backbone; disagree and commit" interview questions

Have backbone; disagree and commit — "Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly.”

Any group of smart leaders will disagree at some point. Amazon wants to see that you know when to challenge ideas and escalate problems to senior leadership. At the same time, they want to know you can sense the right time to move forward regardless of your disagreement.

3.4.5 "Invent and simplify" interview questions

Invent and simplify — "Leaders expect and require innovation and invention from their teams and always find ways to simplify. They are externally aware, look for new ideas from everywhere, and are not limited by “not invented here." Because we do new things, we accept that we may be misunderstood for long periods of time.”

Amazon relies on a culture of innovation. Answering invent and simplify questions is an opportunity to show your ability to create solutions when there is no obvious answer.

You’ll also want to show that you know how to execute big ideas as simply and cheaply as possible. “Think about examples where you've come up with innovative, alternative solutions instead of building a feature that required engineering resources,” Dessy, ex-Amazon interviewer says.

3.4.6 "Dive deep" interview questions

Dive deep — "Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. No task is beneath them.”

When something isn’t working, MLEs need to quickly find a solution. Interviewers want to see that you are excited to dive deep when problems arise.

3.4.7 "Are right, a lot" interview questions

Are right, a lot — "Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.”

Amazon expects its machine learning engineers to produce solutions as quickly as possible and to make a lot of decisions with little information. You’ll want to demonstrate skill in taking calculated risks and show that you're comfortable disproving your own opinions before moving ahead.

3.4.8 "Deliver results" interview questions

Deliver results — "Leaders focus on the key inputs for their business and deliver them with the right quality and in a timely fashion. Despite setbacks, they rise to the occasion and never settle.”

Amazon values action over perfection. When answering questions related to delivering results, you’ll want to indicate that you dislike slipped deadlines and failed goals.

3.4.9 "Think big" interview questions

Think big — "Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers.”

Amazon is huge and its MLEs need to build machine learning solutions that reach significant scale to make a difference for the business. As a result, interviewers will want to see that you can develop and articulate a bold vision.

To demonstrate your ability to ‘think big’, Bilwasiva, Amazon interview coach says: “Share examples of how you've challenged the status quo, pursued innovative ideas, and inspired others to think beyond conventional boundaries.”

3.4.10 "Hire and develop the best" interview questions

Hire and develop the best — "Leaders raise the performance bar with every hire and promotion. They recognize exceptional talent, and willingly move them throughout the organization. Leaders develop leaders and take seriously their role in coaching others. We work on behalf of our people to invent mechanisms for development like Career Choice.”

As mentioned above, Amazon wants new hires to “raise the bar.” Interviewers will want to see that you are not afraid of working with and hiring people smarter than you.

You should also show you enjoy coaching younger colleagues and know how to get the most out of top performers. You’ll notice the examples listed here are general interview questions, but they provide a perfect opportunity for you to address this principle.

This leadership principle is typically discussed in interviews for very senior engineering positions that involve people management or building a team. 

3.4.11 "Frugality" interview questions

Frugality — "Accomplish more with less. Constraints breed resourcefulness, self-sufficiency, and invention. There are no extra points for growing headcount, budget size, or fixed expense.”

At every touchpoint, Amazon tries to provide customers with as much value for as little cost as possible. Interviewers will be looking for how you can support this idea while maintaining a constant drive for innovation.

3.4.12 "Learn and be curious" interview questions

Learn and be curious — "Leaders are never done learning and always seek to improve themselves. They are curious about new possibilities and act to explore them.”

Amazon demands constant improvement in every part of their business. You’ll want to show that you are interested in learning new things and exploring new ideas.

Some examples listed here are general interview questions, but they provide a perfect opportunity for you to address this principle.

3.4.13 "Insist on the highest standards" interview questions

Insist on the highest standards — "Leaders have relentlessly high standards — many people may think these standards are unreasonably high. Leaders are continually raising the bar and drive their teams to deliver high-quality products, services, and processes. Leaders ensure that defects do not get sent down the line and that problems are fixed so they stay fixed.”

Amazon takes the view that nothing is ever “good enough.” They’d like to see that you push for standards that are difficult to meet.

To demonstrate your commitment to high standards, Bilwasiva, Amazon interview coach says to cite examples from your previous roles where you established processes, guidelines, and quality assurance measures to uphold standards and drive continuous improvement.

3.4.14 "Earn trust" interview questions

Earn trust — "Leaders listen attentively, speak candidly, and treat others respectfully. They are vocally self-critical, even when doing so is awkward or embarrassing. Leaders do not believe their or their team’s body odor smells of perfume. They benchmark themselves and their teams against the best.”

The key part of that principle that candidates often miss is the “vocally self-critical” bit. Amazon wants MLEs who focus on fixing mistakes instead of figuring out who to blame.

You’ll want to show that you take action when something is wrong and acknowledge your own faults before blaming other people and teams. 

Bilwasiva, Amazon interview coach also advises discussing “your approach to building relationships based on mutual respect, open communication, and delivering on promises.”

3.4.15 "Strive to be Earth's best employer" interview questions

Strive to be Earth's best employer — "Leaders work every day to create a safer, more productive, higher performing, more diverse, and more just work environment. They lead with empathy, have fun at work, and make it easy for others to have fun. Leaders ask themselves: Are my fellow employees growing? Are they empowered? Are they ready for what's next? Leaders have a vision for and commitment to their employees' personal success, whether that be at Amazon or elsewhere.”

Similar to the principle “hire and develop the best,” this principle is more likely to come up in interviews for senior and/or managerial positions. In this case, you’ll want to show that you’ll not only boost your team, but also create a safe, diverse, and just work environment.

Essentially, if “hire and develop the best” means picking and training a top team, being “Earth’s best employer” means keeping that team safe, enriched, and engaged once you’ve got them.

3.4.16 "Success and scale bring broad responsibility" interview questions

Success and scale bring broad responsibility — "We started in a garage, but we're not there anymore. We are big, we impact the world, and we are far from perfect. We must be humble and thoughtful about even the secondary effects of our actions. Our local communities, planet, and future generations need us to be better every day. We must begin each day with a determination to make better, do better, and be better for our customers, our employees, our partners, and the world at large. And we must end every day knowing we can do even more tomorrow. Leaders create more than they consume and always leave things better than how they found them.”

Amazon wants its employees to understand the responsibility of working for a vast, impactful company.

Show how you measure the impact of your decisions, both in your workspace and in the world around you (e.g. sustainability, justice, etc.). You must always be willing to improve.

Click here for an in-depth guide on how to answer Amazon’s behavioral interview questions.

4. Amazon Machine Learning Engineer Interviewing Tips↑

You might be a fantastic machine learning engineer, but unfortunately, that won’t be enough to ace your interviews at Amazon. Interviewing is a skill in itself, that you need to learn.

Let’s look at some key tips to make sure you approach your interviews in the right way.  

4.1 Ask clarifying questions

Often the questions you’ll be asked will be quite ambiguous, so make sure you ask questions that can help you clarify and understand the problem.

4.2 Be concise but detailed

When answering behavioral questions, start with a short description of a situation you want to cite and be prepared to go into further detail once asked. 

Always use specific information and never generalize. The best way to do this is to prepare a single specific example of a past experience to illustrate your answer to a question.

When talking about your past accomplishments, Bilwasiva, Amazon interview coach advises quantifying your achievements wherever possible. “Use metrics and data to demonstrate the impact of your contributions.”

4.3 Think out loud

You need to walk your interviewer through your thought process before you actually start coding or designing a machine learning system. Amazon also recommends that you talk even while coding as they want to know how you think. Your interviewer may also give you hints about whether you’re on the right track or not. 

4.4 State and check assumptions

In your machine learning system design interview, you need to explicitly state assumptions and check with your interviewer to see if those assumptions are reasonable. 

4.5 Present multiple possible solutions

When you code, present multiple possible solutions if you can. Amazon wants to know your reasoning for choosing a certain solution. 

4.6 Strike a balance between your ambitious and collaborative nature

The ideal Amazon candidate is ambitious and driven, but your interviewer will also want to see evidence of how well you collaborate with others. 

So in your behavioral answers, be sure to get the right balance between ‘we’ and ‘I’. Acknowledge team effort by talking about what 'we' did as a team, and use 'I' to clearly demonstrate your own actions and elaborate on the impact YOU had.

4.7 Center on Amazon’s Leadership Principles

Go deep on Amazon’s Leadership Principles. We can’t stress enough how heavily Amazon emphasizes culture alignment, so be sure to prepare for this as much as you prepare for your technical interviews.

While we mentioned the first four values as the ones given focus in SDE-MLE interviews, the best way to prepare is to have at least one story for each LP.  To be more efficient, you can adapt your stories so they can respond to various leadership principles.

4.8 Make your code organized and production-ready 

Keep your code organized so your interviewer won’t have a hard time understanding what you’ve written. Amazon wants to see that your code has captured the right logical structure.

While your code won’t be tested, you’ll be more impressive if you write testable code. Prepare to explain the Time/Space Complexity of your solutions, and how to better optimize for  Time/Space Complexity.

Also, don’t use random/variable function names. Be sure to write descriptive, meaningful ones.

4.9 Get comfortable with coding on various mediums

Amazon advises MLE candidates to be ready to write code in real-time on an online editor. If the interview is in person, you might be asked to code on paper or a whiteboard. You can check with your recruiter which it will be if you’re not sure which medium to use

5. Preparation plan↑

Now that you know what questions to expect, let's focus on how to prepare. Here are the four most important things you can do to prepare for Amazon's machine learning engineer interviews.

Now that you know what questions to expect, let's focus on how to prepare. Here are the four most important things you can do to prepare for Amazon's machine learning engineer interviews.

5.1 Learn about Amazon's culture

Most candidates fail to do this. But before investing tens of hours preparing for an interview at Amazon, you should take some time to make sure it's actually the right company for you.

Amazon is prestigious, and it's tempting to assume that you should apply without considering things more carefully. But it's important to remember that the prestige of a job (by itself) won't make you happy in your day-to-day work. It's the type of work and the people you work with that will.

If you know machine learning engineers who work at Amazon or used to work there, talk to them to understand what the culture is like. The Leadership Principles we discussed above can give you a sense of what to expect, but there's no replacement for a conversation with an insider. 

We would also recommend checking out the following resources:

• Amazon's technology culture video mix (by Amazon)
• Amazon vision and mission analysis (by Panmore Institute)
• Amazon strategy teardown (by CB Insights)
• The Leadership Principles Explained by Amazon CEO Andy Jassy 
• An Amazon interviewer dives deep into how she uses the Leadership Principles (by Amazon)
• Machine learning research (by Amazon)
• SDE Interview Prep (by Amazon)

5.2 Practice by yourself

As mentioned above, you'll have four main types of interviews at Amazon: coding, machine learning fundamentals, machine learning system design, and behavioral. Here are resources that will help you with each question type.

For coding interviews, we recommend getting used to the step-by-step approach put together by Amazon in the video below.

Here is a summary of the approach:

• Step 1: Clarify
  • Ask clarification questions to remove ambiguity about the problem
  • Explore the edges of the problem
• Step 2: Plan
  • Discuss potential approaches you could take
  • Pick an approach and lay out the high level steps
• Step 3: Implement
  • Write clean code, not pseudocode
  • Comment on your code as you go
• Step 4: Test
  • Start by testing with a simple example
  • Try breaking your code with edge and corner cases
• Step 5: Optimize
  • Calculate time complexity
  • Discuss how you can optimize your solution

For the best resource for prepping for coding interviews, we recommend using our comprehensive coding interview prep guide as your prep launchpad. There you'll find interviewing and preparation tips and links to deep-dive resources on answer methods, data structures, algorithms, and example questions.

If you want to practice algorithms, we recommend using Leetcode, where you can get a lot done on the free tier, or find Amazon-specific questions on the premium tier. Amazon has high standards for its engineers, so practice questions at medium or hard-level difficulty.

For the machine learning system design interviews, we recommend studying our system design interview guide and learning how to answer system design interview questions. These guides cover a step-by-step method for answering system design questions, and they provide several example questions with solutions. 

For the machine learning fundamentals, we recommend that you acquaint yourself with Amazon’s recent machine learning projects and findings by browsing the AWS machine learning blog and the Amazon ML science research blog.

For an end-to-end process to implement machine learning solutions, take a look at this field guide. Although the guide is from Meta, its process breakdown should still be helpful when practicing the example questions we've provided in the section above.

For behavioral interviews, we recommend learning our step-by-step method for answering behavioral questions. Then, you can practice answering the questions listed in section 3.4 above. 

Once you’re in command of the subject matter, you’ll want to practice answering questions. But by yourself, you can’t simulate thinking on your feet or the pressure of performing in front of a stranger. Plus, there are no unexpected follow-up questions and no feedback.

That’s why many candidates try to practice with friends or peers.

5.3 Practice with peers

If you have friends or peers who can do mock interviews with you, that's an option worth trying. It’s free, but be warned, you may come up against the following problems:

• It’s hard to know if the feedback you get is accurate
• They’re unlikely to have insider knowledge of interviews at your target company
• On peer platforms, people often waste your time by not showing up

For those reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert. 

5.4 Practice with experienced ML interviewers

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.

Find an Amazon machine learning engineer interview coach so you can:

• Test yourself under real interview conditions
• Get accurate feedback from a real expert
• Build your confidence
• Get company-specific insights
• Learn how to tell the right stories, better.
• Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!