Waymo Software Engineer Interview Questions + Guide in 2025

Waymo is an autonomous driving technology company dedicated to creating the most trusted self-driving vehicles to enhance mobility and significantly reduce traffic-related fatalities.

In the Software Engineer role at Waymo, you will be responsible for developing and maintaining software solutions that power the autonomous driving systems and enhance the performance of the Waymo Driver. Key responsibilities include designing and implementing scalable data pipelines, collaborating with cross-functional teams to integrate machine learning models, and ensuring the reliability and quality of the software products. Required skills encompass strong programming proficiency in languages such as C++ or Python, experience with large-scale data processing frameworks (like Apache Spark or Google Cloud Dataflow), and a solid understanding of machine learning concepts. Ideal candidates will possess a customer-oriented mindset and the ability to thrive in collaborative environments, reflecting Waymo's commitment to innovation, safety, and teamwork.

This guide will help you prepare for a job interview by providing insights into what to expect and how to effectively showcase your skills and alignment with Waymo's mission and values.

The interview process for a Software Engineer at Waymo is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Recruiter Call

The process begins with a brief phone call with a recruiter. This initial conversation usually lasts around 15-30 minutes and focuses on your background, experience, and motivation for applying to Waymo. The recruiter may ask about your familiarity with the company and the role, as well as your technical skills and previous projects. This is also an opportunity for you to ask questions about the company culture and the specifics of the role.

2. Technical Phone Screen

Following the recruiter call, candidates typically undergo one or two technical phone interviews. These interviews are conducted by software engineers and focus on coding skills, algorithmic thinking, and problem-solving abilities. You may be asked to solve coding problems in real-time using platforms like CoderPad or Google Meet. Expect questions that cover data structures, algorithms, and possibly domain-specific knowledge related to machine learning or computer vision, depending on the team you are interviewing for.

3. Onsite Interviews

Candidates who perform well in the technical phone screens are invited to an onsite interview, which may be conducted virtually or in person. The onsite typically consists of multiple rounds, often four to five interviews, each lasting about 45 minutes. These interviews may include a mix of coding challenges, system design questions, and discussions about your past projects and experiences. Interviewers may also assess your understanding of machine learning concepts, data pipelines, and software architecture, especially if you are applying for roles related to perception or data engineering.

4. Team Match Interviews

After the onsite interviews, candidates may participate in team match interviews. This step involves discussions with potential team members and managers to determine the best fit for both the candidate and the team. It’s an opportunity for you to learn more about the specific projects and challenges the team is facing, as well as to showcase your interest in collaborating with them.

5. Final Review and Offer

Once all interviews are completed, the hiring team will review your performance across all stages. If you are selected, you will receive an offer that includes details about salary, benefits, and other employment terms. If not selected, candidates may receive feedback on their performance, although this can vary.

As you prepare for your interviews, it's essential to be ready for a variety of questions that reflect the technical and collaborative nature of the role. Here are some of the types of questions you might encounter during the interview process.

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

Understand the Technical Landscape

Waymo operates at the intersection of software engineering and cutting-edge technologies like machine learning and computer vision. Familiarize yourself with the specific technologies and frameworks mentioned in the job description, such as C++, Python, Apache Spark, and deep learning frameworks like TensorFlow or PyTorch. Brush up on your knowledge of algorithms, data structures, and system design principles, as these are frequently tested in interviews.

Prepare for Coding Challenges

Expect a rigorous coding interview process that includes multiple rounds of technical assessments. Practice solving LeetCode problems, especially those that focus on graph algorithms, dynamic programming, and data manipulation. Given the emphasis on real-world applications, be prepared to discuss the trade-offs of your solutions and how they can be optimized for performance and scalability.

Showcase Your Collaboration Skills

Waymo values teamwork and collaboration, especially in integrated settings. Be ready to discuss your past experiences working with cross-functional teams, particularly in data engineering or machine learning contexts. Highlight instances where you successfully collaborated with data scientists, product managers, or other engineers to deliver impactful results.

Emphasize Your Problem-Solving Approach

During the interview, focus not just on the final answer but also on your thought process. Interviewers are interested in how you approach problems, break them down, and arrive at solutions. Use the STAR (Situation, Task, Action, Result) method to structure your responses, especially when discussing past projects or challenges.

Be Ready for Behavioral Questions

Waymo's culture emphasizes values such as innovation, safety, and trust. Prepare for behavioral questions that assess your alignment with these values. Reflect on your past experiences and be ready to share examples that demonstrate your commitment to safety, your innovative thinking, and your ability to build trust within a team.

Stay Informed About the Company

Research Waymo's latest developments, projects, and challenges in the autonomous driving space. Understanding the company's mission and how your role contributes to it will help you articulate why you want to work there and how you can add value. This knowledge will also help you ask insightful questions during the interview, demonstrating your genuine interest in the company.

Manage Your Expectations

While many candidates report a positive interview experience, some have noted disorganization in the process. Be patient and proactive in following up with your recruiter if you don’t hear back in a timely manner. This shows your enthusiasm for the role and keeps you informed about your application status.

Practice Mock Interviews

Consider conducting mock interviews with peers or using platforms that simulate technical interviews. This practice can help you become more comfortable with the interview format and improve your ability to articulate your thoughts under pressure.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Software Engineer role at Waymo. Good luck!

In this section, we’ll review the various interview questions that might be asked during a software engineering interview at Waymo. The interview process will likely focus on your technical skills, particularly in programming, machine learning, and system design, as well as your ability to collaborate and communicate effectively within a team.

Programming and Algorithms

1. Can you explain the difference between depth-first search (DFS) and breadth-first search (BFS)?

Understanding these fundamental algorithms is crucial for any software engineering role, especially in a company focused on autonomous driving technology.

How to Answer

Discuss the key differences in their approach, use cases, and performance characteristics. Highlight scenarios where one might be preferred over the other.

Example

“DFS explores as far as possible along each branch before backtracking, making it suitable for problems like pathfinding in mazes. In contrast, BFS explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is ideal for finding the shortest path in unweighted graphs.”

2. Describe a time you optimized a piece of code. What was the original problem, and what changes did you make?

This question assesses your problem-solving skills and ability to improve existing systems.

How to Answer

Provide a specific example, detailing the initial performance issues, the steps you took to optimize the code, and the results of your changes.

Example

“I was working on a data processing pipeline that was taking too long to execute. I identified that a nested loop was causing inefficiencies. By refactoring the code to use a hash map for lookups instead, I reduced the time complexity from O(n^2) to O(n), significantly speeding up the process.”

3. How would you approach designing a data pipeline for processing large-scale data?

Given Waymo's focus on data, this question evaluates your understanding of data engineering principles.

How to Answer

Discuss the components of a data pipeline, including data ingestion, processing, storage, and retrieval. Mention technologies you would use and how you would ensure reliability and scalability.

Example

“I would start by defining the data sources and the required transformations. I would use Apache Beam for data processing, Google Cloud Dataflow for orchestration, and BigQuery for storage. To ensure reliability, I would implement monitoring and alerting systems to catch any failures early.”

4. What is your experience with C++ and Python? Can you compare the two?

This question assesses your programming proficiency and understanding of language-specific features.

How to Answer

Discuss your experience with both languages, highlighting their strengths and weaknesses in the context of software development.

Example

“I have over five years of experience in C++, which I find excellent for performance-critical applications due to its low-level memory management. Python, on the other hand, is my go-to for rapid prototyping and data analysis because of its simplicity and extensive libraries. Each has its place depending on the project requirements.”

5. Can you explain what a binary tree is and how you would traverse it?

Binary trees are fundamental data structures, and understanding them is essential for many algorithms.

How to Answer

Define a binary tree and describe the different traversal methods (in-order, pre-order, post-order) along with their use cases.

Example

“A binary tree is a data structure where each node has at most two children. I would typically use in-order traversal to retrieve data in sorted order, while pre-order is useful for creating a copy of the tree, and post-order is often used for deleting the tree.”

Machine Learning and Data Engineering

1. What is the difference between classification and regression?

This question tests your understanding of basic machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each.

Example

“Classification is used when the output is a category, such as spam detection in emails, while regression is used for predicting continuous values, like house prices based on features. Both are fundamental tasks in supervised learning.”

2. Can you explain what batch normalization does?

This question assesses your knowledge of deep learning techniques.

How to Answer

Discuss the purpose of batch normalization and how it affects the training of neural networks.

Example

“Batch normalization normalizes the inputs of each layer to improve training speed and stability. It helps mitigate issues like internal covariate shift, allowing for higher learning rates and reducing the sensitivity to weight initialization.”

3. Describe a machine learning project you worked on. What challenges did you face?

This question evaluates your practical experience and problem-solving skills in machine learning.

How to Answer

Provide a detailed account of the project, the challenges encountered, and how you overcame them.

Example

“I worked on a project to classify images of traffic signs. One challenge was the imbalance in the dataset. I addressed this by using data augmentation techniques to create more samples of the underrepresented classes, which improved the model's accuracy significantly.”

4. How do you handle missing data in a dataset?

This question tests your data preprocessing skills.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and removal.

Example

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

5. What are some common metrics used to evaluate machine learning models?

This question assesses your understanding of model evaluation.

How to Answer

List and explain various metrics, emphasizing their relevance to different types of problems.

Example

“For classification tasks, I often use accuracy, precision, recall, and F1-score. For regression, I prefer metrics like mean absolute error (MAE) and root mean square error (RMSE) to assess model performance.”

System Design

1. How would you design a system for real-time data processing?

This question evaluates your system design skills and understanding of real-time processing.

How to Answer

Outline the components of a real-time data processing system, including data ingestion, processing, and output.

Example

“I would use a message broker like Kafka for data ingestion, stream processing frameworks like Apache Flink for real-time analytics, and a database like Cassandra for storage. Ensuring low latency and high availability would be key considerations in my design.”

2. Describe how you would approach designing an API for a ride-hailing service.

This question assesses your understanding of API design principles.

How to Answer

Discuss the key features of the API, including endpoints, data formats, and security considerations.

Example

“I would design RESTful endpoints for user authentication, trip requests, and ride status updates. I would ensure that the API returns JSON responses and implement OAuth for secure access. Rate limiting would also be crucial to prevent abuse.”

3. What considerations would you take into account when designing a distributed system?

This question evaluates your knowledge of distributed systems.

How to Answer

Discuss factors such as consistency, availability, partition tolerance, and scalability.

Example

“I would consider the CAP theorem, ensuring a balance between consistency and availability. I would also implement load balancing and redundancy to enhance fault tolerance and scalability, allowing the system to handle increased traffic efficiently.”

4. How would you ensure data quality in a large-scale data processing system?

This question assesses your understanding of data integrity and quality assurance.

How to Answer

Discuss methods for validating and cleaning data, as well as monitoring data quality over time.

Example

“I would implement validation checks at various stages of the data pipeline, such as schema validation and anomaly detection. Regular audits and monitoring dashboards would help maintain data quality and identify issues proactively.”

5. Can you explain the concept of microservices and their advantages?

This question tests your understanding of modern software architecture.

How to Answer

Define microservices and discuss their benefits, such as scalability and maintainability.

Example

“Microservices are an architectural style that structures an application as a collection of loosely coupled services. This approach allows for independent deployment, scaling, and development, making it easier to manage complex applications and improve fault isolation.”