Google Machine Learning Engineer Interview Process

Machine learning has transformed technology as we know it. The adoption of machine learning drives 3x faster execution and 5x faster decision-making. At the helm of today’s machine learning innovation is Google. So when Google sets out to hire machine learning engineers who can contribute to innovations that will change the world, you know they are looking for only the best of the best. 

The good news is that the best of the best are not born; they are made. With the right preparation and guidance, you can land your dream job. If that dream job is to be a machine learning engineer at Google, then we’ve got you covered. 

To better prepare for your next tech interview, check out our technical interview checklist, interview questions page, and salary negotiation ebook to get interview-ready! Also, read How Hard Is It to Get a Job at Google? and How to Get Software Engineering Jobs at Google for specific insights and guidance on Google tech interviews. 

Here’s what you can find in this article: 

• What Is Google's Hiring Criteria for Machine Learning Engineers?
• Google Machine Learning Engineer Interview Stages, Process, and Timeline
• How to Prepare for Google Machine Learning Engineer Interview
• Google Machine Learning Engineer Interview Questions
• Ace Your Google Machine Learning Engineer Interview

What Is Google's Hiring Criteria for Machine Learning Engineers?

To be considered for a machine learning software engineer role at Google, you need to have these minimum qualifications: 

• A bachelor's degree in Computer Science, or a related technical field, or equivalent work experience.
• 5 years of relevant work experience.
• Experience designing and implementing distributed software systems like Java, C++, or Python.
• Research/industry experience in artificial intelligence, ML infrastructure, machine learning (ML) models, natural language processing, or deep learning.

Besides this, Google seeks software engineers who can:

• Build software solutions to real-life problems using machine learning tools. 
• Build systems that can be scaled up to serve billions of users.
• Bring in fresh ideas about information retrieval, large-scale system design, distributed computing, networking and data storage, natural language processing, security, artificial intelligence, UI design, and mobile.
• Display leadership qualities, manage project priorities, deadlines, and deliverables.
• Embody Google’s 8 Artificial Intelligence Principles.

Did you know? The total average compensation of ML engineers at Google is $136,899.

Google Machine Learning Engineer Interview Stages, Process, and Timeline

Google’s Machine Learning Engineer Interview process is similar to that of a Google Software Engineer. The main steps of this process are: 

1. Application Process 

2. Phone Screen + Technical Screen

5. Onsite Interviews

The interview process at Google can last for 6-8 weeks on average. So it will be a good idea to plan and prepare for the long journey ahead.

1. Application Process

Step one is getting a Google interview. You can apply to Google directly or through a recruiter. It will help to have an updated resume and a cover letter tailored to machine learning positions and Google. It would also help your case if you can manage to get an employee referral.

2. Phone Screen + Technical Screen

If your application is selected, you get a call from a recruiter who will use this conversation to get to know you better and assess which team you would be the best fit for. 

Once you get past this first HR screen, the recruiter will then schedule your next interview, which will involve a coding assessment. 

Here’s a coding assessment cheat sheet for you. 

In the coding interview, you will be asked data structure and algorithm questions which you will have to solve on a remote collaborative editor. These questions will be quite similar to the questions you'd come across in a Google Software Engineer interview. 

Onsite Interviews

Onsite interviews are typically 5-6 face-to-face interviews on a variety of topics held at the Google office. Each interview will last about 45-60 minutes and will focus on the following topics:

• Coding interview: Algorithm and data structure questions similar to those you would come across in a Google software engineer interview.
• System design interview: In this interview, you will have to design a high-level modern technology system like a social media platform or a Google feature.
• Machine learning design interview: Here, you will be evaluated on your approach to solving problems using one or a combination of machine learning methods.
• Behavioral interview: These interviews evaluate if your values align with those of Google. Interviewers want to see if you will be a good cultural fit for an ML team in specific, and Google in general. 

For more details, read Google Interview Guide. 

Hiring Decision Process for Google Machine Learning Engineer Interview

Candidates are graded based on a performance feedback form. This form has a summary of the attributes that Google is looking for in a candidate. The interviewers update this form during or after every round. Questions asked in an interview are noted in this form, so the questions aren’t repeated.

Let us look at the main traits that Google is looking for in potential Machine Language   Engineers:

 Cognitive Ability: This is your ability to learn and adapt to strenuous situations. Based on the coding assessments and system design questions, your interviewer tries to evaluate how adept you are at solving problems that seem daunting and how quickly you spot your mistakes and learn from them.

1. Role-Related Evaluation: Interviewers ensure that you have the appropriate experience, competencies, and domain expertise for the position.
2. Leadership Traits: Google is looking for emergent leaders. These are team members most likely to step up and lead a team when the need arises.
3. Cultural Fit: Here, Google wants to make sure you align with the company's values of being collaborative in nature, believing in action, and being comfortable with ambiguity. 

 The final recommendation is made on the lines of strong no hire, no hire, leaning no hire, leaning hire, hire, and strong hire.

 If you come out looking good at the end of this process, your interviewers will submit their feedback, and you will be matched to a team based on your skillset. After a review by the Compensation Committee, you will be made an offer.

Hesitant about negotiating a salary offer? Read The Ultimate Guide to Salary Negotiation at FAANG for Software Engineersto hone your negotiation skills and get an offer that matches your value.

How to Prepare for Google Machine Learning Engineer Interview?

The first step in your tech interview prep should be to make a plan of action. In this section, we’ll cover all the key points you need to consider while planning your Google Machine Learning interview prep.

Important Topics for Google Machine Learning Engineer Interview

We’ve put together some important topics that will help you get started with your Google machine learning engineer interview preparation:

1. Coding Topics

• Programming languages
• Data structure: Arrays, Trees, Stacks, Recursion
• Algorithms: Binary Search, Insertion Sort, Bubble Sort, Selection Sort, Breadth-First Search
• Object-oriented design
• Databases
• Distributed computing
• Operating systems
• Internet topics
  

2. System Design

• Designing complex architecture systems and platforms
• Product features
  

3. Behavioral

• Leadership
• Why Google?
  

4. Machine Learning

• General machine learning and artificial intelligence 
• BigQuery
• TensorFlow
• Cloud Vision
• Natural Language API
• Model validation
• Model optimization
• Machine Learning frameworks
• Framing ML problems
• Architecting ML solutions
• Google Machine Learning Engine
• Automating and orchestrating ML pipelines
• Deep Learning frameworks
• Machine Learning applications

Mock Interviews

It is recommended that you practice at least 30+ mock interviews before sitting down for the actual interview at Google. 

You can practice mock interviews for your Google machine learning engineer preparation with peers, or you can practice them with hiring managers and experts from Google at Interview Kickstart.

Whether you wish to become a FAANG+ machine learning engineer,software developer, machine learning developer, or engineering manager, our mentors and coaches at IK are here to guide you as you set out to prepare for the interview process.

Having guided over 6,000 Software Engineers to land their dream jobs, Interview Kickstart is where you will find everything you need to know about cracking Google’s tech interview process.

For more information, read Google Machine Learning Engineer Interview Prep

Google Machine Learning Engineer Interview Questions

Here are some sample interview questions to get you started:

• You are given the root node of a binary tree T. You need to modify that tree in place, transform it into the mirror image of the initial tree T. (Solution)
• Given an array of integers, find any non-empty subarray whose elements sum up to zero. (Solution)
• Find all palindromic decompositions of a given string s. (Solution)
• Given a binary tree, check if it is a binary search tree. A valid BST does not have to be complete or balanced. (Solution)
• Given a bunch of key-value pairs, for each unique key, find 1) the number of values and 2) the lexicographically greatest value. (Solution)
• You are given an array of n non-negative integers. You need to find the maximum difference between 2 consecutive numbers in the sorted array of the given integers. (Solution)
• Predict the probability of a user finding a given ad useful.
• Design an autocomplete feature using machine learning. 
• Design a scheduler in Python
• Build a model to detect and categorize product defects for a t-shirt manufacturer. 
• Build an object detection model for a small startup company to identify if and where the company’s logo appears in an image.
• Your team has developed an ML model with TensorFlow for a popular massively multiplayer online (MMO) game that predicts the next move of each player. Edge deployment is not possible, but low-latency serving is required. How should you configure the deployment? 

Check out our complete list of system design questions and solved technical questions for more practice questions. 

Ace Your Google Machine Learning Engineer Interview

While the Google machine learning engineer interview process can seem like a daunting task at first, do not fret because help is at hand! If you are confused about how to apply or where to start preparing, sign up for our free webinar and let our experts show you the way.

Our Machine Learning Interview Course is the first-of-its-kind, domain-specific tech interview prep program designed specifically for Machine Learning Engineers. Click here to learn more about the program. 

If you want an edge over other candidates in your Google interview, you should take advice from someone who has experienced the process themselves. If you already know someone at Google, great! But if you’re like most who do not have any connections, we have already done the networking and made connections for you. 

Our courses are taught by FAANG tech leads and seasoned hiring managers. With a cracking team of instructors from FAANG and other tier-1 companies, experienced hiring managers, and tech lead at coveted companies, Interview Kickstart is a powerhouse of expert knowledge and guidance on cracking FAANG interviews.  

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