Waymo Machine Learning Engineer Interview Guide
Updated by Waymo candidates
Written by Graham Carlson, Senior Technical ContributorWaymo’s interview process combines ML fundamentals with high-level coding skills. They expect you to demonstrate strong coding knowledge relative to ML interviews at other organizations.
Their approach includes general coding problems as well as company- and team-specific questions. The process can feel like a hybrid software engineer and ML interview loop.
Waymo will make leveling decisions based on your leadership experience and performance in the behavioral rounds.
They frontload their technical skill assessments, as successful ML candidates must pass a difficult coding screen to reach the final interview loop.
Below, we break down the complete ML engineer interview process at Waymo.
Interview Process
Waymo’s ML Engineer interview process will assess your fit for the specific team and your experience with ML and coding principles. The questions will vary depending on the role and team. Here’s a general outline of how the process can go:
- Recruiter screen: A short call with a recruiter to discuss your background and interest in Waymo
- Technical screen: A difficult assessment of technical skill, typically centered around data structures and algorithms
- Recruiter prep call: Another short call to schedule the final loop and address any questions you have about the interviews
- Final loop: Five interviews, including coding, ML coding, ML design, and two behavioral rounds
Our guides are built from recent, real, first-hand insights shared by both interviewers and candidates.
Processes change. If your experience differs, tell us here–we actively update our information.
Recruiter screen
This is a brief call to discuss your experience, goals, and interest in Waymo. They will also ask about your level of experience as an ML project leader, particularly projects with a high level of complexity.
If you are applying for a team-specific role, they may ask you questions about your direct experience working within that discipline as well.
Recruiter Questions:
Asked at 
Asked at 
Asked at Coding screen
This question will test your coding skills rather than your ML knowledge and will not necessarily pertain to Waymo’s products or the work the team does. They will be expecting you to find a solution with a limited number of hints, and you won’t be able to use a search engine or AI coding assistant.
An example question might require you to create and implement a breadth-first or depth-first search algorithm to analyze a graph or data structure, such as an org chart. They’ll ask follow-ups to test your implementation and the performance of your solution.
Although they may answer a few questions, you will need to show that you have a robust understanding of DSA principles and skills.
Coding Screen Questions:
Create a model of a company org chart and search the model based on names, job titles, and teams
Asked at Meta Asked at Waymo Prep call with recruiter
This is a brief call primarily to schedule the next loop of interviews. You’ll also have a chance to ask questions about the interviews, which you should take advantage of.
It’s important to use this call to understand exactly what is expected of you in each round, as you will need to know if, for example, there is a specific Python library or framework you will be using.
While they won’t give away every detail, knowing the data structures and tools to practice can help you make sure you’re able to answer each question in a way that meets Waymo’s standards.
Final loop
These interviews will be with team members, and there is a lot of flexibility in scheduling, so they may be spread across multiple days depending on their availability. Each interview will last about 45 minutes.
Coding
This coding round will be related to Waymo’s existing projects or to the work the team you are applying for does. For team-specific ML roles, researching their work is a great way to prepare.
For example, you might be asked to analyze a diagrammatic representation of an autonomous driving situation and then be asked to predict the outcome of two objects in the diagram colliding. You would use stack DFS or BFS to generate a set of trajectories and make a prediction.
As with the coding screen, it’s best to spend your time writing code rather than asking questions. If you’re unfamiliar with this sort of work, it may be necessary to clarify things at the start of the round, but you will have limited time, so concision is critical.
Coding Questions:
Using a diagram of several obstacles, figure out how they will collide and what it might look like
Asked at 
Asked at ML Coding
This interview will not necessarily relate to Waymo’s work and will assess your ability to implement algorithms and data manipulation.
You might be given a dataset, usually ~100 rows, and need to extract the relevant parts into a format that allows you to make actionable predictions or probabilities.
They’ll expect you to work with a particular library like NumPy, but if you asked about this earlier in the process, you should have lots of time to prepare.
This interview will cover more general ML topics, such as calculating a mean across multiple dimensions, handling arrays with different shapes, and tokenizing data into a usable format.
ML Coding Questions:
Asked at Perplexity AI • ML system design
This interview will be largely conversational, and while you may take notes and do some light diagramming, you will not be expected to whiteboard your solution. It will not necessarily be related to the team’s work or Waymo’s products.
Typically, you will be asked to design a user-facing algorithm, such as a recommendation tool for a video service. You would need to talk through your approach, classify the data in a usable format, and sort recommendations based on user activity and preferences.
This is a change from the other rounds, which are less focused on discussion. You’ll need to be an active communicator and provide interviewers with insight into the why behind each design choice. They want to see that you can apply a careful, structured approach to your solution.
ML System Design Questions:
Behavioral round (leadership)
This will be a deep dive into one of your projects, exploring the timeline, complexity, collaborative efforts, obstacles, and results. You should be well-prepared with a project already in mind.
When choosing a past project, it’s a good idea to go for those with longer timelines, greater complexity, and ample collaboration, even if the project wasn’t a success. You’ll be asked to speak about what you learned from each challenge and what you would have done differently.
They’ll ask many follow-ups, so it’s a good idea to set clear challenges so you can explain your approach to solving them. You should also discuss any overlap with other team members or teams to highlight your cross-functional experience.
You can let the interviewer steer the conversation, but you can expect to be asked a balance of technical and interpersonal questions, rather than just interpersonal ones. As with the other rounds, the focus will be on high-level technical decision-making.
Behavioral (Leadership) Questions:
Asked at Waymo 
Asked at Asked at Waymo Behavioral round (general)
Rather than a deep dive into a specific project, you will be asked to provide a number of examples of situations where you had to manage conflict, act as a mentor to others, or handle a disagreement over a technical or organizational issue.
The interviewers will also present hypotheticals and ask you to develop a strategy to get another team to buy into your team’s goals. You’ll need to explain the importance of mutuality and how your approach will benefit both teams.
Like the prior round, you will find this discussion balanced between interpersonal and technical topics. As you prepare a list of past conflicts and other examples, you should also revisit the technical details of each one, as they may want to hear those as well.
Behavioral (General) Questions:
Asked at Common Mistakes
- Viewing the process and role as one focused on pure machine learning principles, rather than a hybrid of coding and ML
- Failing to ask detailed questions about the rounds during the recruiter prep call, such as what kind of frameworks and libraries they might use
- Expecting a highly conversational set of interviews rather than one that emphasizes hands-on work and performant code
- Over- or under-preparing for autonomous driving-related questions, as the interviews will be balanced between general questions and Waymo-specific issues
- Choosing a project or example of past work that does not illustrate the ability to handle complexity and collaboration
- Failing to practice coding skills, particularly DSA best practices
Interview Prep
Take a balanced approach: Waymo’s approach does not reward optimizing for a single format or interview style. Certain rounds will be hands-on, while others will be conversational; some will balance technical details with high-level interpersonal issues.
Understand Waymo’s approach to ML: Simply put, machine learning engineers at Waymo need to be able to code, so failing to incorporate coding practice and coaching into your preparations means you’ll struggle to make it to the final loop.
Don’t neglect behavioral prep: Behavioral rounds are crucial to leveling, and ML engineer candidates will be leveled up or down based on their experience and ability to reassess their work history. Preparing and practicing a list of examples of projects, conflicts, and mentorships can help you reach your desired level.
About the Role
What do you work on as an ML engineer at Waymo?
- Work on the forefront of autonomous driving: You’ll build models and tools, recommend improvements, and troubleshoot new paradigms to support autonomous driving.
- Draw from a wide range of techniques: using AI, reinforcement learning, large-scale analysis of visual data, and more, you’ll help reduce the need for human intervention and improve triage for autonomous vehicles.
- Scale up: You’ll support Waymo’s growth as they increase the size of their fleet, analyze new regions and cities, and develop new hardware to support demand for autonomous driving.
Core Responsibilities
Waymo’s ML Engineers are focused on interacting with, understanding, and executing all the data relevant to autonomous driving. This includes modeling, reinforcement learning, training AI models, and data collection. Here are some general tasks:
- Develop simulations for the training of autonomous vehicles in a multitude of different environments and situations
- Assess and improve the interaction between human monitoring and issue triaging using advanced analysis and automation techniques
- Build tools that proactively monitor and recursively improve the safety and efficiency of Waymo’s fleet
- Contribute to research projects that form the foundation of autonomous driving best practices
Compensation
Waymo’s ML Engineers make an average $386,000 in total compensation.
Job Requirements
Experience
Waymo ML Engineers are expected to have at least 5 years of experience in ML, particularly with Python and ML frameworks such as TensorFlow, PyTorch, or NumPy. Strong coding skills are also essential to get through the interview process. Experience with AI models, autonomous vehicles, and reinforcement learning is required for higher-level roles.
Education
For entry-level ML roles, education is not required, but higher-level roles require a master’s or PhD in a relevant field or extensive experience.
Resources
- Waymo Careers
- Working at Waymo
- Waymo Interview Questions
- Machine Learning Interview Prep
- Generative AI Interview Prep
- Machine Learning Interview Questions
FAQs
How long is the Waymo ML Engineer interview process?
As a rapidly growing company, Waymo’s process is not overly defined, and the exact timing can vary quite a bit. Most report the process taking between 4 and 6 weeks, possibly longer, as the final round may be spread over multiple days.
Does Waymo have internships?
Waymo offers internships primarily during the summer months and holidays. You can see internship positions on their careers page.
Does Waymo offer remote work opportunities?
Depending on the role, Waymo supports in-person, hybrid, and remote work. They have offices across multiple continents, and their decision may depend on regional needs.
How long should I wait after a rejection before reapplying to Waymo?
There is no guideline on this on their website, but the best practice is to wait at least 6 months before reapplying. If you were rejected for a specific reason, such as an inability to relocate, it’s acceptable to reapply if that changes.
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