Interview Guide Jul 22
Jul 223 rounds
As a Machine Learning Engineer at Google, you would work on building and improving the algorithms and systems that power many of Google's products and services. Your responsibilities may include developing and implementing machine learning models, analyzing large datasets, and designing and running experiments to evaluate the performance of machine learning systems.
In addition to technical skills, Google values strong communication and collaboration skills. As a Machine Learning Engineer, you would work closely with other engineers, data scientists, and product managers to understand user needs and develop solutions that meet those needs.
Google's machine learning engineers work in various areas of the company, each focusing on improving different aspects of Google's products and services. For example, a Software Engineer, Machine Learning would work on developing and scaling machine learning systems for various Google products and services. In contrast, a Machine Learning Engineer, Google Health would focus on developing machine learning models and systems to improve healthcare outcomes. A Machine Learning Engineer, Search Ranking would work on developing and optimizing machine learning models that improve Google's search results. Meanwhile, a Research Scientist, Machine Learning would be responsible for conducting research in machine learning and developing new algorithms and models to improve Google's products and services.
These are just a few examples of the Machine Learning Engineer positions available at Google. The specific responsibilities and qualifications for each position may vary, so you should review the job descriptions carefully.
How to Apply for a Machine Learning Engineer Job at Google?
To apply for a Machine Learning job at Google, you will need to visit Google's career website and search for open Machine Learning positions. Once you have found a position that you are interested in, you will be able to submit an application online. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates.
For example, a Software Engineer, Machine Learning would work on developing and scaling machine learning systems for various Google products and services. In contrast, a Machine Learning Engineer, Google Health would focus on developing machine learning models and systems to improve healthcare outcomes - you can highlight any past experience related to the type of MLE role you are applying for.
If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters from Google will give you feedback on your resume.
As a part of the Google MLE interview, the candidate will need to go through multiple interview rounds:
1. Phone Screening - The phone screening will be a conversation with a recruiter, detailing your ML background, your past relevant projects, and a quick assessment of your skill sets based on your resume.
2. Second Round of Interviews - The second round of the interview process is an extensive evaluation of your technical and professional skills. This interview round consists of multiple rounds designed to assess different aspects of your expertise and capabilities.
3. Onsite Interview Rounds - The final primary round is an onsite interview process that consists of several rounds consisting of HR and VP interviews along with DSA round and ML System Design round.
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The first round typically begins with an initial phone conversation with a recruiter. During this call, the recruiter will delve into your technical background, past relevant projects, and a quick assessment of your skills based on your resume. The interviewer will then focus on assessing your ML skills. You will be asked about your experience in developing ML models, selecting appropriate algorithms, and working with various ML frameworks and tools.
- Why do you want to join Google?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Google?
- Tell me about your machine learning background.
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The second round for the Machine Learning Engineer role at Google is a multi-round interview process. The interview process consists of several rounds, and you can expect to face one or more of the following rounds:
- ML Domain Round: In this round, the interviewer will evaluate your expertise in the domain of machine learning. You can expect questions related to the fundamental concepts of machine learning, various ML algorithms, and their applications. The interviewer may also ask you to solve a few problems related to machine learning and explain your approach. Some candidates also reported that there were questions related to Computer Vision and NLP in the round.
- Coding Round: This round will evaluate your coding skills. You can expect to face consecutive coding rounds, where the interviewer will ask you to solve coding problems from platforms like LeetCode. You will be evaluated on your ability to write efficient and optimized code, use appropriate data structures, and handle edge cases.
- Behavioral Interview: In this round, the interviewer will evaluate your soft skills and behavioral traits. You can expect questions related to your previous work experience, how you have handled difficult situations in the past, and how you work in a team. The interviewer may also ask you situational questions to evaluate your problem-solving skills.
The rounds may be conducted on different days or back-to-back on the same day, depending on the availability of the interviewers and your schedule.
- How do you decide which algorithm to use for a particular machine learning problem?
- Explain the concept of regularization in machine learning.
- Can you explain the ROC curve and its importance in machine learning?
- What is cross-validation, and how is it useful in machine learning?
- Explain the difference between bias and variance in machine learning.
- Write a tool/function for reversing a string.
- Given an array of integers, find two numbers that add up to a specific target number.
- Implement a binary search tree and its basic operations.
- Write a program to find the longest palindrome in a given string.
- Given the egos you may have worked with in academia, describe a time you worked with someone who is difficult to work with.
- Explain a project you worked on and how you collaborated with your team members to achieve the project goals.
- How do you stay up-to-date with the latest trends and developments in the field of machine learning?
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The final primary round is an onsite interview process that consists of several rounds. You can expect to face one or more of the following rounds:
- DSA Round: In this round, the interviewer will evaluate your problem-solving skills, coding ability, and proficiency in data structures and algorithms. You can expect to solve coding problems related to machine learning and data science concepts. The interviewer may also ask you to explain your approach and optimize your solution.
- HR Interview: In this round, the interviewer will evaluate your soft skills and behavioral traits. You can expect questions related to your previous work experience, your interest in machine learning, and why you want to work at Google. The interviewer may also ask situational questions to evaluate your problem-solving skills and how you handle difficult situations.
- VP Interview: In this round, you will be interviewed by a Vice President of the department or the hiring manager. The interviewer will evaluate your leadership skills, domain expertise, and suitability for the role. You can expect questions related to your previous work experience, your ability to lead teams, and how you handle ambiguity and uncertainty.
- ML System Design Round: In this round, the interviewer will evaluate your ability to design scalable and efficient machine learning systems. You can expect questions related to data processing, model training, testing, and deployment. The interviewer may also ask you to design a system that can handle a large amount of data and can scale to meet the demands of a production environment. You may also be asked to explain the trade-offs between different system design choices, including specific load balancers.
Please note that this round is role specific and you might encounter a different case depending on the role you are applying for.
- Write a function to find the maximum element in a list of integers using binary search.
- What is the time complexity of training a support vector machine?
- Tell me about a time when you had to collaborate with a difficult colleague or stakeholder.
- What do you think are the biggest challenges facing the machine learning industry today?
- How would you design a system to process a large amount of unstructured data from multiple sources?
- Can you explain the process of training and deploying a machine learning model in a production environment?
- How would you design a system to monitor the performance of a machine learning model in real-time?
- Can you explain the difference between a load balancer and a reverse proxy, and when you would use each one?
When you are preparing for a MLE interview - we’d recommend keeping the following in mind:
- Research about Google's company culture, values, and goals to align them with your career aspirations. You can check out About Google to know more about the company culture.
- Be prepared to talk about your experience and background in machine learning, as well as your knowledge of ML frameworks and tools. Make sure to highlight your relevant skills and experiences that match the job requirements.
- Be ready to answer technical questions related to the fundamentals of machine learning, including algorithms, regularization, cross-validation, and bias-variance tradeoff. Prepare thoroughly and practice coding problems from platforms like LeetCode.
- Brush up on your problem-solving skills, as you will likely face several coding problems during the interview rounds.
- Be ready to talk about your previous work experience and how you have collaborated with team members and stakeholders to achieve project goals. Make sure to highlight your soft skills and behavioral traits that align with Google's values.
- Stay up-to-date with the latest trends and developments in the field of machine learning. Research the latest ML research papers and keep yourself informed about new frameworks and tools.
- During the onsite interview rounds, be prepared to talk about your leadership skills, domain expertise, and ability to design scalable and efficient machine learning systems.
- Last but not the least, Googley-ness. Whether you are trying to land a PM role at Google, preparing for the Google Engineering Manager Interview or interviewing for Google Software Engineer, this is a common round that you HAVE to prepare for.
Responsibilities of a Machine Learning Engineer at Google
The responsibilities of a Machine Learning Engineer at Google across roles can broadly be seen as-
- Write product or system development code. The engineer would be responsible for writing code for a machine learning system, using programming languages such as Python or TensorFlow.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Propose, design, and implement high-performance ML platform solutions that significantly advance the deployment of models that serve millions of users.
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
Skills and Qualifications needed for Machine Learning Engineers at Google
Some of the skills and qualifications that may be required for a Machine Learning Engineer at Google include:
- It's beneficial to have at least 2+ years of experience in software development roles.
- You should have experience with the tools and frameworks commonly used in machine learning, such as TensorFlow. In addition, it's important to have a solid understanding of artificial intelligence, deep learning, and natural language processing.
- Developing technologies that are accessible to all users is crucial. Experience in this area will help you design and build machine learning systems that can be used by people with diverse abilities and needs.
- It's important to have a good grasp of data structures and algorithms. This will help you understand how machine learning algorithms work, and how to optimize them for performance.
- Experience testing, and launching software products, and experience with software design and architecture will be crucial.
- As a machine learning engineer, you'll likely be working on cross-functional projects within a large, matrixed organization. Having experience with this type of work environment will be beneficial, as you'll need to be able to collaborate effectively with colleagues from a range of different backgrounds and disciplines.
The average salary for a Machine Learning Engineer at Google is around $175,000 per year, with a range of $130,000 to $225,000 per year depending on experience and other factors. It's important to note that salaries can vary based on a number of factors such as location, specific job responsibilities, and level of experience.
The interview process for a MLE role at Google typically includes 3 primary rounds - a phone screening, second round of interviews, and the final onsite interview rounds. The phone screening will be a conversation with a recruiter, detailing your ML background, your past relevant projects, and a quick assessment of your skill sets based on your resume. The second primary round of the interview process is an extensive evaluation of your technical and professional skills. This interview round consists of multiple rounds designed to assess different aspects of your expertise and capabilities. The final primary round is an onsite interview process that consists of several rounds consisting of HR and VP interviews along with DSA round and ML System Design round.