Verily Life Sciences Machine Learning Engineer Interview Questions + Guide in 2025

Verily Life Sciences is a subsidiary of Alphabet Inc. focused on developing technologies to improve healthcare and disease management through data-driven insights.

As a Machine Learning Engineer at Verily, you will be tasked with designing, implementing, and optimizing machine learning models that drive innovative health solutions. Key responsibilities include developing algorithms that process complex datasets to extract valuable insights, collaborating with interdisciplinary teams to translate healthcare challenges into technical solutions, and ensuring the integrity and scalability of algorithms in production environments. Success in this role requires a strong foundation in algorithms and programming, particularly in Python, as well as experience in machine learning frameworks. Ideal candidates will possess a blend of analytical skills, creativity in problem-solving, and a passion for improving health outcomes through technology. Familiarity with statistics and SQL will further strengthen your candidacy.

This guide aims to equip you with the necessary insights and preparation strategies to excel in your upcoming interview for the Machine Learning Engineer position at Verily Life Sciences.

The interview process for a Machine Learning Engineer at Verily Life Sciences is structured and thorough, reflecting the company's commitment to finding the right talent.

1. Initial Screening

The process begins with an initial phone screening, typically lasting around 30 minutes. This call is conducted by a recruiter who will review your application and discuss your interest in the role. During this conversation, you will have the opportunity to highlight your relevant skills and experiences, as well as learn more about the company culture and expectations.

2. Technical Screening

Following the initial screening, candidates will undergo a technical screening, which usually lasts about 45 minutes. This interview is often conducted via video call and focuses on coding skills, particularly in algorithms and data structures. Expect to solve coding problems that may include array manipulations and other algorithmic challenges, similar to those found on platforms like LeetCode.

3. Onsite Interviews

The next step involves a series of onsite interviews, which are typically conducted virtually. This stage consists of multiple technical interviews, often four rounds, each lasting around 45 minutes. Interviewers will assess your problem-solving abilities through coding challenges and may also delve into your understanding of machine learning concepts, statistics, and Python programming. Be prepared for questions that require you to demonstrate your thought process and approach to solving complex problems.

4. Final Interview with Hiring Manager

The final step in the interview process is a discussion with the hiring manager. This interview focuses on behavioral questions and assesses your fit within the team and the company. You may be asked about your past projects, how you handle challenges, and your motivations for wanting to join Verily.

As you prepare for your interviews, it's essential to be ready for a mix of technical and behavioral questions that will evaluate both your technical expertise and your ability to work collaboratively within a team.

Next, let's explore the specific interview questions that candidates have encountered during the process.

Here are some tips to help you excel in your interview.

Prepare for a Structured Interview Process

The interview process at Verily Life Sciences is well-organized and follows a structured format similar to that of major tech companies. Expect an initial phone screen with a recruiter, followed by a technical screening, and then multiple rounds of technical interviews. Familiarize yourself with this structure and prepare accordingly. Make sure to communicate your availability clearly and be prompt in your responses to maintain a good rapport with the recruitment team.

Master the Technical Skills

As a Machine Learning Engineer, a strong command of algorithms is crucial, as it constitutes a significant portion of the interview focus. Brush up on your algorithmic knowledge, particularly around data structures, recursion, and time complexity. Practice coding problems on platforms like LeetCode, especially those tagged as medium to hard, as these are commonly featured in interviews. Additionally, ensure you are comfortable with Python, as it is a key language for this role.

Showcase Problem-Solving Abilities

During the technical interviews, you will likely face coding challenges that require you to demonstrate your problem-solving skills. Approach these problems methodically: clarify the requirements, outline your thought process, and communicate your reasoning as you code. Interviewers appreciate candidates who can articulate their thought process, so don’t hesitate to think aloud and ask clarifying questions if needed.

Prepare for Behavioral Questions

Behavioral questions are an integral part of the interview process. Be ready to discuss your past experiences, particularly those that highlight your teamwork, leadership, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that demonstrate your fit for the role and the company culture.

Understand the Company Culture

Verily Life Sciences is known for its mission-driven approach and focus on impactful projects. Familiarize yourself with the company’s values and recent initiatives. Be prepared to discuss why you are interested in working at Verily and how your personal values align with the company’s mission. This will not only show your enthusiasm but also help you assess if the company is the right fit for you.

Stay Engaged and Ask Questions

Throughout the interview process, engage with your interviewers by asking insightful questions about the team, projects, and company culture. This demonstrates your genuine interest in the role and helps you gather valuable information to make an informed decision if an offer is extended. Be sure to prepare thoughtful questions that reflect your research about Verily and the specific team you are interviewing with.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview and reiterate your interest in the position. This small gesture can leave a positive impression and keep you top of mind as the hiring team makes their decisions.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Verily Life Sciences. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Verily Life Sciences. The interview process will likely focus on your technical skills, problem-solving abilities, and how you approach machine learning challenges. Be prepared to discuss algorithms, coding, and your experience with data handling and analysis.

Algorithms

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, including how supervised learning uses labeled data while unsupervised learning does not. Provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as classification tasks using algorithms like decision trees. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, such as clustering with K-means.”

2. How do you evaluate the performance of a machine learning model?

This question assesses your understanding of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, while precision and recall are crucial for imbalanced datasets. For binary classification, I often use the F1 score to balance precision and recall, and ROC-AUC for a comprehensive view of performance across thresholds.”

3. Describe a time you had to optimize a machine learning model. What steps did you take?

This question gauges your practical experience with model optimization.

How to Answer

Outline the steps you took, including data preprocessing, feature selection, and hyperparameter tuning.

Example

“In a project to predict customer churn, I first cleaned the dataset and performed feature selection using recursive feature elimination. Then, I used grid search for hyperparameter tuning, which improved the model’s accuracy by 15%.”

4. What is overfitting, and how can you prevent it?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well, and I apply regularization methods like L1 or L2 to penalize overly complex models.”

5. Can you explain the concept of bias-variance tradeoff?

This question tests your understanding of model performance dynamics.

How to Answer

Discuss the tradeoff between bias and variance and how it affects model performance.

Example

“The bias-variance tradeoff is a fundamental concept in machine learning. High bias can lead to underfitting, while high variance can cause overfitting. The goal is to find a balance where the model performs well on both training and unseen data.”

Python and Coding

1. Write a function to implement a basic linear regression model from scratch.

This question assesses your coding skills and understanding of linear regression.

How to Answer

Explain your approach to implementing the algorithm, including the cost function and gradient descent.

Example

“I would start by defining the cost function as the mean squared error and then implement gradient descent to minimize this cost. The function would take in training data and return the learned parameters.”

2. How would you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation or removal.

Example

“I handle missing data by first analyzing the extent of missingness. If it’s minimal, I might impute using the mean or median. For larger gaps, I consider removing those records or using more advanced techniques like K-nearest neighbors imputation.”

3. Can you explain how you would implement a decision tree algorithm?

This question tests your understanding of decision trees and your coding ability.

How to Answer

Outline the steps to build a decision tree, including splitting criteria and stopping conditions.

Example

“To implement a decision tree, I would recursively split the dataset based on the feature that provides the highest information gain until a stopping condition is met, such as a maximum depth or minimum samples per leaf.”

4. What libraries do you commonly use for machine learning in Python?

This question assesses your familiarity with relevant tools.

How to Answer

Mention popular libraries and their use cases.

Example

“I commonly use libraries like Scikit-learn for general machine learning tasks, TensorFlow and Keras for deep learning, and Pandas for data manipulation and analysis.”

5. How do you ensure your code is efficient and scalable?

This question evaluates your coding practices and understanding of performance.

How to Answer

Discuss techniques for writing efficient code, such as algorithm complexity analysis and using appropriate data structures.

Example

“I ensure my code is efficient by analyzing the time and space complexity of algorithms. I also use data structures like hash tables for quick lookups and optimize loops by minimizing nested iterations.”

Statistics and Probability

1. What is the Central Limit Theorem, and why is it important?

This question tests your understanding of statistical concepts.

How to Answer

Define the Central Limit Theorem and explain its significance in statistics.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample statistics.”

2. How do you handle outliers in your data?

This question assesses your data analysis skills.

How to Answer

Discuss methods for detecting and handling outliers, such as Z-scores or IQR.

Example

“I handle outliers by first identifying them using Z-scores or the interquartile range (IQR). Depending on the context, I may choose to remove them, transform the data, or use robust statistical methods that are less sensitive to outliers.”

3. Explain the difference between Type I and Type II errors.

This question tests your knowledge of hypothesis testing.

How to Answer

Define both types of errors and their implications in statistical testing.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for interpreting the results of hypothesis tests accurately.”

4. What is a p-value, and how do you interpret it?

This question evaluates your understanding of statistical significance.

How to Answer

Define p-value and explain its role in hypothesis testing.

Example

“A p-value measures the probability of observing the data, or something more extreme, given that the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading to its rejection.”

5. How do you assess the correlation between two variables?

This question tests your knowledge of correlation analysis.

How to Answer

Discuss methods for assessing correlation, such as Pearson’s correlation coefficient.

Example

“I assess the correlation between two variables using Pearson’s correlation coefficient, which quantifies the linear relationship between them. A value close to 1 or -1 indicates a strong correlation, while a value near 0 suggests no correlation.”