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

The good news is that the right preparation can make a big difference and can help you land an ML job at Google. We have put together the ultimate guide below, to help you maximize your chances of success. Special thanks to our expert MLE coach Vivek for his insights.

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

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

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

1. Google Machine Learning Engineer Role and Salary↑

Before we cover your Google ML engineer interview process and questions, let’s take a look at the role first.

1.1 What does a Google Machine Learning Engineer do?

“At Google, everything revolves around making a significant impact. Your focus should be on creating solutions that address real-world problems, then refining and scaling them for maximum reach.” Vivek, machine learning engineer at Google.

For a Google machine learning engineer, your means of making an impact is developing and optimizing machine learning models and algorithms, using these techniques to solve various problems across Google’s products and services. 

Most Google ML engineers are hired as software engineers with a particular focus on machine learning, deep learning, and artificial intelligence. Your day-to-day will include writing and optimizing code for training, deploying, and integrating AI/ML models into products or services in collaboration with other software engineers. You’ll also be tasked with designing and conducting experiments with ML researchers and data scientists.

As a Google ML engineer, you’re expected to take full ownership of your projects from conception to completion. “This means taking charge, making key decisions, and ensuring successful outcomes,” Google engineering coach Vivek explains.

The ML/DL tools available to you at Google and the standard of coding are second to none. The success of Google as an organization is driven by its strong engineering culture and its early embrace of machine learning. It is considered to be a career pinnacle for many engineers, especially for those in machine learning.

1.2 How much does a Google Machine Learning Engineer make?

Based on Glassdoor data, the median total pay for a Google machine learning engineer is $280k, 41% 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:

• Google India ML engineer: est. average total pay $33.5k
• Google US ML engineer: est. average total pay $280k

Most ML engineering posts at Google are software engineer posts. So to give you an idea of how much Google ML engineers make at the company, we’ve pulled the SWE 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 ex-Google interview coaches can provide such a significant return on investment.

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

2. Google Machine Learning Engineer Interview Process and Timeline↑

2.1 What steps to expect

What's the interview process and timeline at Google for a machine learning engineer role? It normally follows the steps below and takes about two to three months to complete:

• Resume screen
• Google Hiring Assessment/s 
• Recruiter screen (~30 min)
• Technical screen (1 or 2 interviews, 45-60 min each)
• Onsite interviews (5 rounds, 45-60 min each)

Next, we'll dig into each of these steps in more detail. If you're interviewing with multiple companies, take a look at our guides to the Meta ML engineer interview and the Amazon ML engineer interview.

2.1.1 Resume screen

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, as millions of candidates do not make it past this stage. So take extra care to tailor your resume to the specific position you're applying to. 

Check out our free Google resume guide with examples for help on writing yours. If you’re looking for expert feedback, get input from our team of ex-FAANG recruiters who will cover what achievements to focus on (or ignore), how to fine-tune your bullet points, and more.

2.1.2 Google Hiring Assessments

The next step in your application is to answer the Google Hiring Assessment. You’ll be receiving a link to this with instructions on how to answer and the deadline. 

The Google Hiring Assessment, based on candidates’ reports, is a sort of behavioral or personality test, therefore there’s no right or wrong answer. Some receive an optional module, which is a sort of extension of the assessment, but it’s completely voluntary (it shouldn’t affect the result of your application at all).

You may also receive additional assessments. For engineering roles, for instance, you may receive a coding exercise. You can practice for this using the questions we’ll cover in the example interview questions below.

2.1.3 Recruiter screen

In most cases, your first interview after passing the assessments will be with an HR recruiter on the phone. They are looking to confirm that you've got a chance of getting the job at all, so be prepared to explain your background and why you’re a good fit at Google. You should expect typical behavioral and resume questions, like "Tell me about yourself," "Why Google?" or "Tell me about your current project."

If you get past this first HR screen, the recruiter will schedule your technical phone screen(s). They will usually let you know who your interviewers are and what type of interviews you should expect and will share resources to help you prepare for them.

2.1.4  Technical screen 

Next, you'll have one or two problem-solving/coding interviews, during which you'll be asked primarily data structure and algorithm questions. These questions tend to be quite similar to the questions you'd encounter as a Google software engineer.

Your interviewer, who may be a hiring manager or a peer, may start with a few behavioral questions, but most of the time will be spent on coding questions.  You'll share a Google Doc with your interviewer, write your solution directly in the document, and won't have access to syntax highlighting or auto-completion like you would in a regular IDE. It's therefore a good idea to practice writing code in Google Docs before your interview.

2.1.5 Onsite interviews 

Finally, if you pass your technical screens, you’ll be invited to the onsite interviews. These are the real test. You'll typically spend a full day either in a virtual interview or at a Google office and do five interviews in total. Each interview will last about 45 minutes and cover one of the following topics:

• Coding interview, where you'll solve algorithm and data structure questions similar to those you'd encounter in a software engineer interview.
• Machine learning domain, where you’ll be asked about your machine learning knowledge and practical experience.
• Machine learning system design interview, where you'll need to suggest an approach for how to solve a problem using a machine learning solution.
• Behavioral interview, where you can expect questions about your background, accomplishments, and your motivation for applying to Google.

While you’re onsite, you’ll typically have one or two coding interviews, one machine learning domain interview, one machine learning system design interview, and a behavioral round. However, the exact breakdown will depend on the exact team and role that you’re applying for.

2.2 What the Google 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 Google looks for in a candidate. That form is constantly evolving, but we have listed the main components we know of at the time of writing this article below.

A) Questions asked

In the first section of the form, the interviewer fills in the questions they asked you. These questions are then shared with your future interviewers so you don't get asked the same questions twice.

B) Attribute scoring

Each interviewer will assess you on the four main attributes Google looks for when hiring:

• General cognitive ability. This is often referred to as "GCA" by Googlers. The company wants to hire smart engineers who can learn and adapt to new situations. Here, your interviewer will try to understand how you solve hard problems and how you learn. For more information, take a look at our guide to the Google GCA interview.
• Role-related knowledge and experience. This is often referred to as "RRK" or "RRKE" internally. The company wants to make sure that you have the right experience, domain expertise, and competencies for the position you're applying for. For more information, take a look at our guide to the Google RRK interview.
• Leadership. Google looks for a particular type of leadership called “emergent leadership.” You'll typically be working in cross-functional teams at Google, and different team members are expected to step up and lead at different times in the lifecycle of a project when their skills are needed.
• Googleyness (i.e. culture fit). The company wants to make sure Google is the right environment for you. Your interviewer will check whether you naturally exhibit the company's values, including being comfortable with ambiguity, having a bias to action, and having a collaborative nature.

Depending on the exact job you're applying for, these attributes might be broken down further. For instance, "role-related knowledge and experience" could be broken down into "deep learning" or “natural language processing" for certain roles. But the total number of attributes does not usually exceed six or seven.

In this middle section, Google's interviewers typically repeat the questions they asked you, document your answers in detail, and give you a score for each attribute (e.g. "Poor," "Mixed," "Good," "Excellent").

C) Final recommendation

Finally, interviewers will write a summary of your performance and provide an overall recommendation on whether they think Google should be hiring you or not (e.g. "Strong no hire," "No hire," "Leaning no hire," "Leaning hire," "Hire," "Strong hire").

2.3 What happens behind the scenes

If things go well at your onsite interviews, here is what the final steps of the process look like:

• Interviewers submit feedback
• Hiring committee recommendation
• Team matching
• Senior leader and compensation committee review
• Final executive review (only senior roles)
• You get an offer

After your onsite, your interviewers will all submit their feedback usually within two to three days. This feedback will then be reviewed by a hiring committee, along with your resume, internal referrals, and any past work you have submitted. At this stage, the hiring committee will make a recommendation on whether Google should hire you or not.

If the committee recommends that you get hired, you'll usually start your team-matching process. In other words, you'll talk to hiring managers and one or several of them will need to be willing to take you in their team in order for you to get an offer from the company.

In parallel, the hiring committee recommendation will be reviewed and validated by a senior manager and a compensation committee who will decide how much money you are offered. Finally, if you are interviewing for a senior role, a senior Google executive will review a summary of your candidacy and compensation before the offer is sent to you.

As you've probably gathered by now, Google goes to great lengths to avoid hiring the wrong candidates. This hiring process with multiple levels of validations helps them scale their teams while maintaining a high caliber of employees. But it also means that the typical process can spread over multiple months.

3. Google 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 for the machine learning engineer role at Google:

• Coding
• Machine learning domain
• 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

Google’s machine learning software engineers solve some of the most difficult problems the company faces with code. It's therefore essential that you have strong problem-solving skills to stand out as a candidate. 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. You can expect to have 2 to 3 coding interviews.

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

1. Graphs / Trees (39% of questions, most frequent)
2. Arrays / Strings (26%)
3. Dynamic programming (12%)
4. Recursion (12%)
5. Geometry / Maths (11% of questions, least frequent)

Below, we've listed common examples used during Google software engineer 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 this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.

3.2 Machine learning domain interview

As our Machine learning engineer coach Vivek said, Google values candidates with real-world machine learning experience. This is where you’ll be asked to demonstrate that. 

Depending on the level you’re applying for, you may be asked about the basics or asked to speak about an ML/DL project you’ve worked on. Based on what you’ve included in your resume, your interviewer may also ask you about the specifics of your focus area like recommendation engines, NLP Vision 4, etc. 

Below are some example machine learning domain questions we’ve gathered from Google candidates, along with a few more from other machine learning engineer interviews reported on Glassdoor and Reddit.

3.3 Machine learning system design interview

Google has huge data sets and billions of users across its various apps. 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. There may be some coding involved during machine learning rounds as well (e.g. “write code for an email auto-response system”).

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 develop a machine learning solution and talk about the pipeline (data collection, preprocessing, model arch, loss function, optimization, serving, and monitoring). 

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 Google's machine learning design interview: both the initial question and possible follow-ups as you build out the model. These questions are from Glassdoor and Blind. Note that we've modified the phrasing in some cases to make the questions more clear.

3.4 Behavioral interview

For the behavioral interview, and sometimes at the beginning of your technical interviews, you'll be asked behavioral or "resume" questions. These questions focus on your past work experience, your qualifications, and your motivation for applying to Google. In other words, it's a way for your interviewer to get to know you better.

Behavioral questions are a great opportunity to tell your story, highlight your top qualifications, and demonstrate your “Googleyness.” You should also be ready to drill down into the technical details of the projects on your resume and discuss the types of projects you'd like to work on in the future.   

In addition, if you're interviewing for management or senior positions, you'll also usually have leadership interviews where you'll be assessed on your people and project management skills. People management questions tend to dive into how you would support and grow your team, while project management questions tend to dive into how you would effectively lead projects end-to-end.

Below, we've listed several example behavioral questions that were asked in either Google machine learning SWE or Google software engineer interviews, according to data from Glassdoor. 

Go deep into Google’s culture by reading our guide on Googleyness & leadership interview questions. Then practice with our step-by-step method for answering behavioral questions.

4. Google Machine Learning Engineer Interviewing Tips↑

You might be a fantastic ML engineer, but unfortunately, that won’t necessarily be enough to ace your interviews at Google. 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. Most of the questions will focus on testing your technical proficiency.

4.2 Think out loud

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

During your technical interviews, you’ll also be assessed on your communication skills, or the way you talk through your problem-solving process. Use this as your opportunity to show how well you communicate and collaborate with colleagues by treating your technical interview like a conversation. 

4.3 State and check assumptions

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

4.4 Start with the most optimal solution if possible

You won’t have enough time to present multiple solutions in your 45-minute interview, so if possible, this successful Google candidate advises to present your most optimal solution first. If not, go with a correct one then work toward optimization. Again, be sure to talk your way through your thought process during either of these cases.

4.5 Center on Google’s values/Googleyness

Never skip out on reviewing Google’s values and culture as you’ll be asked behavioral questions even during your technical interviews. In fact, it might be good to review them even before your recruiter screen, as you’ll most likely be asked behavioral/culture fit questions during this part of the interview process.

Prepare anecdotes/concrete examples from your professional experience corresponding to each Google core value. To be more efficient, adapt your stories so they can respond to different values.

4.6 Keep your code organized 

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

4.7 Get comfortable with coding on various mediums

Google now typically asks interviewees to code in a Google doc. But this can vary, it could be on a physical whiteboard or a virtual one. Check with your recruiter what it will be and practice it a lot. 

4.8 Brush up on Python 

You’ll find that most machine learning-related roles at Google will require experience with Python, as it is Google’s programming language of choice for machine learning.

During your ML interview, Google MLE coach Vivek says that you should “be prepared to implement fundamental ML techniques (e.g., Convolution, Batch Normalization, Logistic Regression) in pure Python.” This will show your interviewers that you can code ML solutions from the ground up.

That said you’ll still find machine learning roles that are open to candidates with experience in other coding languages.

5. Preparation plan↑

Now that you know what questions to expect, let's focus on how to prepare. After all, the right preparation will make the difference between failing your Google interviews and getting an offer.

Here are the four most important things you can do to prepare for Google's machine learning engineer interviews.

5.1 Learn about Google's culture

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

Google is prestigious and it's therefore 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 engineers who work at Google or used to work there, it's a good idea to talk to them to understand what the culture is like. In addition, we would recommend checking out the following resources:

• Google's mission statement (by Google)
• Google's values (by Google)
• Google strategy teardown (by CBS Insights)
• Google’s (Alphabet’s) Organizational Culture & Its Traits (by Panmore Institute)
• Google for Developers’s Machine Learning Courses 
• Google Design’s People + AI Research section

5.2 Practice by yourself

As mentioned above, you'll have four main types of interviews at Google: coding, system design, machine learning 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 described by Google in the video below.

Here's a summary of the approach:

1. Ask clarification questions to make sure you understand the problem correctly
2. Discuss any assumptions you're planning to make to solve the problem
3. Analyse various solutions and tradeoffs before starting to code
4. Plan and implement your solution
5. Test your solution, including corner and edge cases

If you're looking 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.

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, while not focused on machine learning, should help you develop a good way to structure your answers.

Additionally, we recommend this machine learning field guide for an end-to-end process to implement machine learning solutions. 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. If you have more time to prepare, then you can prepare even more "stories" summarizing your top qualifications or important lessons that you've learned.

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 a Google 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!