Google is a frontrunner when it comes to driving innovation. So, it is only natural that when it sets out to hire artificial intelligence engineers, Google only wants the best of the best. Google looks for engineers who are driven and passionate about changing the world. People who want to make it a better place with their AI and machine learning skills. If you would like to be at the front seat of innovative technologies that will transform the world as we know it, you need to give your best shot at the Google artificial intelligence engineer interview.
If you are preparing for a Google artificial intelligence engineer 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.
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We have put together everything you need to know before starting your prep for the Google artificial intelligence engineer. Here’s what will find in this article:
- What Does a Google Artificial Intelligence Engineer Do?
- Skills and Qualifications Required to be an Artificial Intelligence Engineer at Google
- Google Artificial Intelligence Engineer Interview Process and Timeline
- Topics to Prepare for Google Artificial Intelligence Engineer Interview
- Sample Google Artificial Intelligence Engineer Interview Questions
- Google Artificial Intelligence Engineer Career FAQs
What Does a Google Artificial Intelligence Engineer Do?
Artificial engineers at Google work with the Google cloud platform. They help customers build and transform the best systems for their business using technologies built-in in the Google cloud platform.
Here’s what your role as a Google artificial intelligence engineer will look like:
- Ensuring that Google products are engineered for scalability, security, and reliability.
- Understanding the needs of Google’s customers and helping them use technology to build their businesses.
- Designing and implementing AI solutions that involve customer use cases.
- Leveraging core Google products such as DataFlow, AI Platform, And TensorFlow.
- Identifying opportunities for customers to apply AI in their businesses.
- Deploying solutions and delivering workshops to educate customers.
- Working closely with product engineering, product management teams, software developers, and engineering managers towards building and driving excellence in Google’s products and services.
- Supporting proper implementation of Google Cloud Products with the help of best practices, architecture guidance, capacity planning, and data migration.
- Providing enterprise-grade cloud solutions to companies by leveraging Google’s innovative technologies.
Your responsibilities as an artificial engineer at Google will include:
- Being a trusted technical advisor to customers and solving complex AI challenges.
- Creating and delivering best practices suggestions, blog articles, tutorials, technical presentations, and sample code while you adapt to different kinds of key technical and business stakeholders.
- Delivering tailored solutions by working with partners, customers, and Google Product teams.
- Coaching customers on potential challenges in artificial intelligence systems, including feature definition, monitoring, data validation, and feature management.
- Traveling frequently for delivering onsite activities, technical reviews, and meetings.
Skills and Qualifications Required to be an Artificial Intelligence Engineer at Google
Before you begin your prep for the AI engineer interview at Google, ensure you have the right qualifications and skills.
- A bachelor’s degree in computer science, mathematics, a related technical field, or equivalent practical experience.
- Experience in directly building AI solutions.
- Experience in coding in one or more languages (such as Scala, Python, Java) with experience in algorithms, data structures, and software design.
- Experience in working with technical customers.
- A master's degree or Ph.D. in computer science, artificial intelligence, machine learning, or a related technical field.
- 2 years of relevant work experience in ML/AI software development and architectures for artificial intelligence with a focus on deep learning.
- Experience in building, deploying, and improving artificial intelligence models and algorithms in real-world products.
- Foundational Coding: C++, Python, PHP/Hack, Golang, and Java
- Data science
- System design and software architecture
- Data structures
- Server backend distributed and parallel systems
- Full-stack development (frontend and backend)
- Scalable enterprise platforms and applications
- Application security and incident management
- Information retrieval or natural language processing
- Data science
- Deep learning
- Cloud offerings
- Big O
- API development
- Project management
- Google cloud platform
- Team management
In addition to the minimum and preferred qualifications, it will be useful to familiarize yourself with Google’s 8 artificial intelligence principles that form the basis of all its AI initiatives.
Interviewers often want to see how well your values align with Google’s, especially in behavioral interviews. You can practice these interviews with hiring experts at Interview Kickstart. Sign up for this FREE webinar to get started today!
Google Artificial Intelligence Engineer Interview Structure
A typical Google artificial intelligence engineer interview process involves the following steps:
- Application Process
- Phone Screen + Technical Screen
- Onsite Interviews
The interview process at Google can last for 6-8 weeks on average from when your application is accepted for further rounds. Let’s look at all the stages of the interview:
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 the artificial intelligence positions and Google. It would also help your case if you can manage to get an employee referral.
Phone Screen + Technical Screen
If your application is shortlisted, you will get a call from a recruiter; this is called the phone screen. During this initial conversation, the recruiter will try to know you better and assess your skillset to place you in the right team. 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. You will be required to solve these programming problems with a remote collaborative editor. These questions will be quite similar to the questions you'd come across in a Google Software Engineer interview.
Here’s a coding interview cheat sheet to help you prepare for coding interviews.
Onsite interviews are typically 5-6 face-to-face interviews on various 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 encounter 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 using artificial intelligence concepts.
- Behavioral interview: Here, your interviewers will discuss your previous work experiences and your motivation behind joining Google in this round. Interviewers want to see how well you know yourself. They also want to see how well you can collaborate across teams. It will be helpful to have some concrete examples and anecdotes handy.
Check out the complete Google Interview Guide for more information.
For tips on how to clear the behavioral interviews, check out the Behavioral Interview Guide.
Topics to Prepare for a Google Artificial Intelligence Engineer Interview
Here are some topics to help you in your Google AI engineer interview prep:
- Designing complex architecture systems and platforms
- Product features
Artificial Intelligence Topics:
- General machine learning and artificial intelligence
- Model validation
- Model optimization
- ML/AI frameworks
- Framing AI problems
- Architecting AI solutions
- Designing data preparation and processing systems
- Monitoring, optimizing, and maintaining AI solutions
- Deep learning frameworks
- Artificial intelligence applications
- Bayesian inference
- PyTorch CNN feature extraction
- AIC and BIC
- Common loss functions for object detection
- Information theory
- Logistic regression
- Automatic differentiation
Sample Google Artificial Intelligence Engineer Questions
Here are some sample interview questions to get you started
- Given a variety of 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)
- Find all palindromic decompositions of a given string s. (Solution)
- Given a positive integer n, return ALL strings of length 2*n with well-formed round brackets. (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 ad.
- If removing missing values from a dataset causes bias, how will you deal with the situation?
- Why use feature selection?
- What is Rectified Linear Unit in machine learning?
- Design a recommendation engine for jobs.
- What is the difference between a boosted model and a bagged model?
- What is the AdaGrad algorithm?
- What is the degree of freedom for lasso?
- What are anomaly detection methods?
- What is AUC in machine learning?
Also, check out our complete list of system design questions and solved technical questions to master your Google Artificial Intelligence Engineer tech interview prep.
Google Artificial Intelligence Engineer Interview Tips
Here are some additional tips to help you ace your Google artificial intelligence engineer interview:
- Plan your interview preparation so that you have ample time to cover all topics and practice mock interviews. If you do not know how to make an interview prep plan, you can take the help of experts.
- Practice rethinking and redesigning Google products that already exist, focusing on security.
- Practice interview-style coding questions on a whiteboard without using a compiler.
- Practice mock interviews with yourself, your peers, or with FAANG hiring managers at 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
- Sign up with Interview Kickstart to practice interviews with experienced coaches, hiring managers, and tech leads from FAANG companies.
Google Artificial Intelligence Engineer FAQs
1. What is the difference between a Google artificial intelligence engineer and a Google machine learning engineer?
Google machine learning engineers are a subset of Google software engineers. AI engineers are software engineers specializing in artificial intelligence. An artificial intelligence engineer uses artificial intelligence algorithms to solve real-life problems and build software, while a machine learning engineer uses machine learning techniques to solve the same problems.
2. What is the timeline for the Google AI Engineer Interview process?
A typical Google artificial intelligence engineer Interview process goes on for 6-8 weeks once your application is shortlisted for further interview rounds.
3. How much does a Google artificial intelligence engineer make?
An average Google artificial intelligence engineer makes about $105,000 per year, along with bonuses, stock grants, perks, and benefits. Click here to learn more.
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Land Your Dream Google Artificial Intelligence Engineer Role with Interview Kickstart
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