Google is not just one of the world’s largest tech platforms but also one of the most coveted employers. Google is known to go out of its way to take care of its employees, ensuring a well-rounded life centered around mutual growth. Because of this reputation, jobs at Google always see the highest number of applicants. The same is true for the post of Google machine learning engineer.
If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation e-book 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.
We have put together everything you need to know to prepare for a machine learning engineer interview at Google. Here’s what you’ll find in this article:
A Google machine learning engineer is responsible for researching, building, and designing artificial intelligence systems that run on their own to automate predictive models. Some of your responsibilities as a Google machine learning engineer will be to:
A Google machine learning software engineer is responsible for designing, building, productionizing ML models and using Google Cloud technologies along with other reliable ML models and techniques to solve various business challenges.
To become a machine learning engineer, Google recommends that you should have at least 3+ years of hands-on experience working with cloud products and solutions. Here are some other areas you should consider working on before applying for the role:
Pro Tip: Take the Google Professional Machine Learning Engineer Certification exam to up your chances of qualifying for this role.
Machine Learning enables a computer to learn on its own or with little initial help. It uses four broad types of algorithms: Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning.
Artificial Intelligence uses three main techniques: Searching techniques, knowledge representation, and reasoning.
A machine learning engineer uses Machine learning techniques to solve real-life problems and build software. An artificial intelligence engineer uses artificial intelligence algorithms to solve the same problems.
A typical Google machine learning engineer interview process is similar to that of a Google Software Engineer. The main steps of this process are:
The interview process at Google can last for 6-8 weeks, on average. So it will be a good idea to prepare yourself not just for the interview but 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.
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 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:
Now that you’re familiar with the Google machine learning engineer interview process, we can discuss what the interview is really like. A typical Google ML engineer interview will be quite similar to that of a Google software engineer interview. Most interview rounds remain the same - the coding interview, system design interview, and the behavioral interview.
What’s different is that the machine learning design interview will involve outlining a high-level approach for a system or a real-life problem. You will be expected to develop a machine learning solution. Google deals with huge data sets across billions of users. You will be evaluated on your ability to apply machine learning solutions to real-life problems of this magnitude.
Sounds challenging, doesn’t it? That is the thrill of a Google ML interview that attracts the best data science talent from all over the world. To make things sweeter, we should tell you that as a machine learning engineer, you will be able to negotiate a higher salary package than other software engineers at Google.
Salary negotiation is a must-have skill. Read The Ultimate Guide to Salary Negotiation at FAANG for Software Engineers to hone your negotiation skills and get an offer that matches your value.
We’ve put together some topics you should pay attention to during your ML tech interview prep. Here they are:
Data Science has become very popular among engineers in the past decade. And rightly so, as it promises to be the future of technology. While skill lists keep changing with time, here are some essential skills needed to be a machine learning engineer at Google::
Here are a few sample coding interview questions that you can expect at Google:
Be sure to include the following tips in your prep plan to take preparation to the next level:
1. What do Machine Learning Engineers do at Google?
Machine learning engineers are at the front seat of innovation at Google. They use machine learning and deep learning frameworks to solve real-world problems.
2. How is a Google Machine Learning Engineer different from a Google Software Engineer?
Google machine learning engineers are a subset of Google software engineers. Essentially, ML engineers are software engineers specializing in machine learning.
2. What is the timeline for the Google Machine Learning Engineer Interview process?
A typical Google machine learning engineer interview process goes on for 6-8 weeks.
There’s good news and bad news. The bad news is that the road to bagging a Google machine learning engineer job is long and tedious. The good news is that you do not have to do it alone!
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If you are confused about how to apply or where to start preparing, sign up for our free webinar and let the experts show you how it's done.