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.
Abhishek Gunasekar, who transitioned his SWE career into FAANG+, has some wonderful things to talk about Interview Kickstart
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:
- What Does a Google Machine Learning Engineer Do?
- How to Become a Machine Learning Engineer at Google
- Difference Between Google Machine Learning Engineer and Artificial Intelligence Engineer
- Google Machine Learning Engineer Interview Process and Timeline
- What’s the Google Machine Learning Engineer Interview Like?
- Topics to Prepare for Google Machine Learning Engineer Interview
- Skills Required to be a Machine Learning Engineer at Google
- Sample Google Machine Learning Engineer Interview Questions
- Google Machine Learning Engineer Interview Tips
- Google Machine Learning Engineer Career FAQs
- How to Ace the Google Machine Learning Engineer Interview
What Does a Google Machine Learning Engineer Do?
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:
- Develop next-generation technologies that change how billions of users access and use information.
- Design major software components, systems, and features.
- Design, develop, test, deploy, maintain, and improve machine learning framework and infrastructure.
- Optimize models for production deployment.
- Partner closely with other engineering teams to ensure organizational alignment.
- Be versatile, demonstrate leadership qualities, and take on new problems enthusiastically across the full stack.
- Review Google’s products to build centralized solutions, break down barriers, and strengthen existing Google systems.
How to Become a Machine Learning Engineer at Google
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:
- You should be capable of inculcating responsible AI practices throughout the ML development project.
- You should also be proficient in model architecture, metrics interpretation, and data pipeline interaction.
- You should be familiar with the foundational concepts of data engineering, infrastructure management, application development, and data governance.
- You should have a thorough understanding of training, deploying, retraining, scheduling, monitoring, and improving machine learning models.
- As a Machine Learning Engineer, you should be able to design and create scalable AI solutions for optimal performance.
Pro Tip: Take the Google Professional Machine Learning Engineer Certification exam to up your chances of qualifying for this role.
What Is the Difference Between a Google Machine Learning Engineer and an Artificial Intelligence Engineer?
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.
What's a Typical Google Machine Learning Engineer Interview Structure?
A typical Google machine learning engineer interview process is similar to that of a Google Software Engineer. The main steps of this process are:
- Application Process
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.
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.
3. 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.
What’s a Google Machine Learning Engineer interview Like?
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.
Topics to Prepare for a Google Machine Learning Engineer Interview
We’ve put together some topics you should pay attention to during your ML tech interview prep. Here they are:
- 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
- Distributed computing
- Operating systems
- Internet topics
- System Design
- Designing complex architecture systems and platforms
- Product features
- Behavioral Topics
- Why Google?
- Machine Learning Topics
- General machine learning and artificial intelligence
- Model validation
- Model optimization
- Machine Learning frameworks
- Framing ML problems
- Architecting ML solutions
- Designing data preparation and processing systems
- Automating and orchestrating ML pipelines
- Monitoring, optimizing, and maintaining ML solutions
- Deep Learning frameworks
- Machine Learning applications
Skills Required to be a Machine Learning Engineer at Google
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::
- Foundational Coding
- Data Science
- Deep Learning
- Cloud Offerings
- System Design & Software Architecture
- Data Structures
- Big O
- API Development
- Project Management
- Google Cloud Platform
- Team Management
Sample Google Machine Learning Engineer Interview Questions
Here are a few sample coding interview questions that you can expect at Google:
- Find all palindromic decompositions of a given string s. (Solution)
- Given various coin types defining a currency system, find the minimum number of coins required to express a given amount of money. Assume an infinite supply of coins of every type. (Solution)
- Sort a given singly linked list in ascending order. (Solution)
- Design a scheduler in python.
- Predict the probability of a user clicking on a given post.
- How would you build a system that detects if a given media is offensive?
Google Machine Learning Engineer Interview Tips
Be sure to include the following tips in your prep plan to take preparation to the next level:
- Your interviewer will give you a vague interview problem. This is an opportunity for you to ask for details and specifics so that your solution is as close to their expectations as possible. Ask questions about system requirements here and determine what customer base and scale you’re building for.
- Practice some tough interview-style coding questions on a whiteboard without using a compiler.
- Look at Google and practice rethinking and redesigning Google features that already exist.
- Practice mock interviews with yourself, your peers, or a complete google interview prep guide like Interview Kickstart.
- When practicing, make it a point to think out loud and explain your thought process to the interviewer.
- Start timing yourself when you practice system design questions. A lot of attention is given to how you manage your time and how efficiently you come up with the solution.
- Sign up with Interview Kickstart to practice interviews with experienced coaches, hiring managers, and tech leads from FAANG companies.
Google Machine Learning Engineer Career FAQs
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.
3. 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.
How to Ace Your Google Machine Learning Engineer Interview
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!
Having trained over 6,000 software engineers, we know what it takes to crack the toughest tech interviews. Since 2014, Interview Kickstart alums have been landing lucrative offers from FAANG and Tier-1 tech companies, with an average salary hike of 49%. The highest ever offer received by an IK alum is a whopping $933,000!
Interview Kickstart offers interview preparation courses 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 leads at coveted companies, Interview Kickstart is a powerhouse of expert knowledge and guidance on cracking FAANG interviews.
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.