Google Machine Learning Engineer (MLE) Interview Guide

At the heart of every cutting-edge Google product is a dedication to complex problem-solving and innovative brand storytelling. Working toward Google’s mission to “organize the world's information and make it universally accessible and useful,” many Googlers are driven by an interest in data science and user research—especially those in the exploding field of machine learning and AI.

If you’re passionate about advancing the future of computer science on a global scale, Google might be an excellent fit for you.

After all, Google is one of the leading educators of IT, AI, and machine learning engineering (MLE) through its Grow with Google and Google for Developers programs.

In this guide, you’ll learn about what Google MLEs do and how to ace the Google MLE interview.

What does a Google MLE do?

Machine learning at Google generally falls into two categories:

research-driven data science roles, and
product-driven engineering roles.

Your day-to-day depends on which type of role you apply for. A research scientist at Google Research working in Human-Computer Interaction and Visualization might spend their days developing experiments and implementing new design innovations into prototypes to analyze and publish a research paper.

In contrast, a Cloud AI engineer might spend their day designing and implementing machine learning solutions for customer use cases directly related to their product team.

Learn more about careers in Google Research.

Before applying, ensure the role you’re applying for best suits your engineering background.

Google often posts hundreds of machine learning roles on its careers page, and applying to a position with relevant experience can help strengthen your chances of moving forward.

Many MLEs at Google get hired under the software engineer umbrella.

There are a variety of levels for Google SWEs based on education and industry experience, ranging from L3 to L10. Per Levels.fyi, the current compensation packet for Google SWE/MLEs is as follows:

For entry-level SWEs at L3, total compensation is $184K with a $144K base salary, $30K in stock, and a $9K bonus.
For mid-level SWEs at L4, total compensation is $276K with a $172K base salary, $80K in stock, and a $24K bonus.
For senior-level SWEs at L5, total compensation is $341K with a $203K base salary, $113K in stock, and a $25K bonus.

What are the typical job requirements for a Google MLE?

Education: The level of higher education required by Google depends on the seniority of the position and relevant experience. Instead of firmly requiring advanced degrees, Google welcomes candidates with bachelor’s degrees and strong career experience.

Although there is no required major for MLE roles, Google suggests studying a quantitative field such as:

Statistics
Computer Science
Mathematics
Data Science
Engineering
Physics
Economics

The exception to this is Google Research jobs, which generally require graduate and postgraduate degrees due to their proximity to academia.

Education requirements for ML/AI research positions depend on the specific niche you’re applying to, but as with engineering roles, Google prefers candidates with a background in a quantitative field.

Additionally, showing that your previous academic work aligns with Google’s research philosophy is an excellent way to set yourself apart.

Experience: In many early-career and mid-level SWE/MLE job listings, Google often lists fewer required years of experience than similar companies. This requirement demonstrates Google’s interest and willingness to invest in younger candidates with exciting potential and curiosity.

All senior-level positions at Google require more years of experience, but younger candidates can pursue the possibility of growing at Google through an entry-level role.

When thinking about how to showcase your experiences, consider the quality and ease of performing tasks over the number of years you’ve been doing them.

Prepare to explain what you know confidently and show a willingness to learn. Also, prepare to demonstrate technical skills in any required coding languages, deep learning frameworks, and reporting/analytics tools for your prospective role.

Since every MLE role at Google has unique requirements, we’ve collected a few examples for reference below. Here are the requirements for a MLE working on the Google Pixel Camera team:

Required:

B.S. in computer science (or related field)
1+ years developing software in one or more general-purpose programming languages (Python, C++)
Experience with data structures and/or algorithms in an academic or industry setting

Preferred:

M.Sc. or PhD in computer science (or related field)
Experience programming in C, C++, or Python
Ability to apply machine learning algorithms in imaging domains
Familiarity with Camera Sensors, ARM and/or other low-power SoC architectures

Here are the requirements for a Cloud AI engineer working on Google Cloud Global Delivery Services:

Required:

B.S. in computer science, mathematics, or a related technical field
3+ years of experience building ML solutions
Experience coding in one or more languages (e.g., Python, Scala, Java, Go, or similar), data structures, algorithms, and software design
Ability to communicate with technical customers

Preferred:

2+ years of experience working with recommendation engines, data pipelines, or distributed machine learning
Experience with deep learning frameworks (e.g., Tensorflow), Vertex AI, or Generative AI
Knowledge of technical consulting, machine learning operations, and BigQuery machine learning
Familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance

Recommendations before you apply for Google MLE roles

Know Google AI. From Google for Developers to Google Research to Google Cloud to Grow with Google, the company creates and curates countless products in the ML/AI ecosystem. As a prospective candidate, ensure you’re well-versed in the latest Google AI innovation, particularly within the ecosystem you’re applying to.
One step at a time. The Google interview loop might have a reputation for being long and rigorous, but don’t let that overwhelm you. Consider why you’d be a good fit for the role you’re applying for. Take the time to build a custom resume for the position. This self-reflection will ultimately strengthen your application.
Think quality, not quantity. Although Google often lists many open positions, take time to ensure the job(s) you’re applying to best fits you. While applying to several jobs at once might be tempting, doing so won’t make you stand out positively. Instead, invest in 1-3 roles you feel genuinely excited about.

You can apply to up to 3 Google job listings every 30 days. Google allows reapplications after a rejection, but only after a year has passed to grow your technical skill set.

Interview Process

Google’s interview process features a decent variety depending on the specific role and team you’re applying for. However, a standardized overview of the engineering loop is as follows:

An (optional) online technical assessment, such as a coding quiz to test your skills
1-2 phone screenings to assess your understanding of ML/AI concepts as well as whether you’d be a good culture fit, generally facilitated by a recruiter, hiring manager, or prospective team member
An onsite interview loop of 3-4 interviews on your knowledge of ML domain and coding concepts

Depending on your location and prospective role, the interview loop may be in-person or virtual (generally on Google Meet).

Online Technical Assessment

The first stage of many Google MLE interviews is a short quiz concerning the skill set required for your prospective role. Prepare to answer an easy-medium coding question related to the qualifications listed in Google’s job posting.

Check out Google’s Tech Dev Guide with coding question resources.

Any technical skills listed on your resume are things you could comfortably do in a structured quiz. Depending on your role, you might not have this entry assessment, but if you’re applying for a product-driven engineer role, be prepared to test your skills before the full interview process begins.

Check out some of our favorite Google coding resources:

Phone Screening

After an initial technical screen, expect 1-2 phone interviews with a recruiter, hiring manager, or prospective team member. These interviews gauge whether you would be a good culture fit at Google. Be prepared to answer questions that assess your “Googleyness” and articulate past MLE experiences.

“Googleyness” is a collection of traits Google seeks during hiring. It represents a spirit of innovation, communication, and volunteering. Check out Google’s mission and vision statements to strengthen your sense of the company’s culture and aspirations.

Here are some screening questions you can expect:

Learn more about behavioral interviews for MLE/SWEs.

Onsite Interview Loop

After the preliminary screening, the onsite interview loop consists of 3-4 technical interviews covering the ML domain and coding. Google generally schedules all of these interviews over one day. They may be in-person or virtual.

ML Domain

Research, data, and algorithms are the backbone of all Google products. As an MLE candidate, your ability to be part of the future of machine learning and generative AI at Google is at the forefront of your application.

If you’re applying for a niche data science role, ensure you’ve reviewed the research area you're applying to.

If you’re applying for an ML/AI software engineer role, make sure you’ve researched the product ecosystem you’d be working in. Be ready to talk about ML/AI and your previous work experiences.

In these interviews, Google is looking for:

Passion and familiarity with ML/AI concepts and problem-solving
Ability to articulate ML solutions to a variety of stakeholders
Understanding of Google’s current ML/AI resources

Curiosity is a core tenet of Googleyness. Prepare for an engaging dialogue with your interviewer(s) to show your communication skills, but make sure you answer the questions efficiently. Exploring the problem-solving process is critical, but for a company designing complex global technology like Google, the solution is, too.

Here are some ML domain questions you can expect:

Coding

Coding is essential to any MLE role at Google. This technical interview centers around your ability to problem-solve quickly and efficiently. The content of this interview and the coding languages used will differ depending on your role, but a dedication to curiosity, communication, and complex code is key.

In these interviews, Google is looking for:

Ability to talk through your technical process in both complex and simple terms
Proficiency in the coding languages/concepts required by the job posting
Mindfulness of working on a limited time frame

Google is looking for candidates with the hard skills to be a successful MLE and the ability to work efficiently on a multi-disciplinary team. Showing that you can communicate your process, ask questions if you need clarification, and take a deep breath when you need to will only help you during the interview.

Here are some coding questions you can expect:

Take advantage of pre-existing Google content, such as Google’s computer science resource library, an archive of interview questions, and learning content for developers.

Tips and Strategies

Use the “XYZ formula” on your resume. Providing precise quantitative results can help explain the impact of your work in past roles. To that end, Google’s hiring team advises using the XYZ formula on your resume: “I accomplished [X], as measured by [Y], by doing [Z].”
Focus on the data. From speaking about your past achievements to analyzing ML metrics and algorithms, always remember to highlight the data. Google values team members who can dissect, interpret, and make the most of a given data set.
Ask questions. It can feel scary to ask questions, especially in a technical interview, but showing a dedication to understanding what is being asked of you and an interest in the company goes a long way. Do your research beforehand, but don’t be afraid to dig deeper into concepts that interest you or ask for clarification.
Prepare your space. With the rise of hybrid and virtual employees, it’s possible that all of your interviews will be virtual. There’s no downside to having only virtual interviews in the hiring process, but ensure that an unexpected technical issue doesn’t hinder your ability to communicate effectively with your interviewer. Check your WiFi, video camera, and microphone before the meeting to minimize the likelihood of connectivity or communication issues. Additionally, take your interviews in a quiet and professional space.

Check out Google’s Interview Tips.

Additional Resources

FAQs

Can I interview again if I’m rejected? Yes! However, for all engineering and machine learning roles, Google requires candidates to wait 12 months before reapplying to gain additional experience. Consider taking this time to cultivate and develop a new skill, such as proficiency in a new machine learning framework or discipline.
Does Google offer ML internships? Interns are a huge part of Google’s culture and dedication to the next generation of STEM. Google has an extensive internship program, including SWE/MLE roles.