# Get a Job at Waymo: Interview Process and Top Questions Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/blog/waymo-interview-process Source observed: 2026-11-05T02:09:48.089Z Last checked: 2026-11-05T02:09:48.089+00:00 Indexed: 2026-11-07T03:44:04.398+00:00 --- # Get a Job at Waymo: Interview Process and Top Questions We created this guide to help you prepare for Waymo interviews and understand how to pursue a job there. It covers Waymo's hiring stages, ML engineering interview formats, example questions, preparation advice, and links to our related interview resources. Aced is formerly Exponent. ![Aced Team](https://s3-us-west-2.amazonaws.com/pmlesson/2019/11/exponent-logo-fb.jpg) *Aced Team • Last updated a month ago* ## About Waymo ### What is Waymo? Waymo is an autonomous-driving technology company and a subsidiary of Alphabet, Google's parent company. It began as Google's self-driving car project in 2009 and became a standalone company in 2016. Waymo operates Waymo One, a commercial robotaxi service in Phoenix, San Francisco, Los Angeles, and Austin. ### Where is Waymo located? Waymo is headquartered in Mountain View, California, with engineering offices across the Bay Area. It also operates in Phoenix, Los Angeles, Austin, and has a growing international presence that includes London. The guide describes Waymo as employing over 2,500 people. Browse open roles on the [Waymo careers page](https://careers.withwaymo.com/?ref=exponent-blog.ghost.io). ### Who does Waymo hire? Waymo hires [software engineers](https://www.aced.io/courses/software-engineering?ref=exponent-blog.ghost.io), [machine learning engineers](https://www.aced.io/courses/ml-engineer?ref=exponent-blog.ghost.io), robotics engineers, [product managers](https://www.aced.io/courses/pm?ref=exponent-blog.ghost.io), and operations specialists. ML roles require both strong software-engineering skills and deep machine-learning knowledge. Candidates may pass a coding screen but struggle with ML system design, or have research strength but fail a LeetCode-style coding round. Learn about Waymo's [culture and teams](https://careers.withwaymo.com/working-at-waymo?ref=exponent-blog.ghost.io). Its teams include Perception, Prediction, Planning, Simulation, and Infrastructure. ### Waymo interview resources - [Waymo Machine Learning Engineer Interview Guide](https://www.aced.io/guides/waymo-machine-learning-engineer-interview?ref=exponent-blog.ghost.io) - [Waymo Interview Questions](https://www.aced.io/questions?company=waymo&ref=exponent-blog.ghost.io) - [Waymo Interview Experiences](https://www.aced.io/experiences?company=waymo&ref=exponent-blog.ghost.io) - [Waymo Company Hub](https://www.aced.io/companies/waymo?ref=exponent-blog.ghost.io) ## Waymo interview process The process follows a Google-style structure: 1. **Recruiter phone screen:** A brief introductory call about your background and interest. 2. **Technical coding screen:** A medium-to-hard data-structures-and-algorithms problem. 3. **Recruiter prep call:** A scheduling and preparation call before the final loop; you can ask what each round covers. 4. **Final interview loop:** Five to six rounds covering coding, ML coding, ML system design, and two behavioral interviews. Expect approximately **4 to 6 weeks** from application to offer. After the initial technical screen, the final loop may be spread across multiple days for scheduling flexibility. There is **no take-home assignment**; evaluation occurs through live interviews. The ML-engineering process includes traditional coding and ML-specific rounds and is described as the same for senior and staff candidates. Leveling occurs after the loop. Staff candidates must show leadership on high-impact, long-duration projects involving cross-team coordination. Strong technical performance without the required leadership bar can result in a senior offer instead. ## Recruiter screen The recruiter screen is a brief introductory and logistical call about your background, current work, interest in Waymo, and experience leading complex ML projects. Prepare a concise explanation of why Waymo specifically, research the team you are applying to, and connect your background to the relevant domain. ## Technical coding screen The technical screen uses a traditional data-structures-and-algorithms format at medium-to-hard LeetCode difficulty. One candidate received a BFS problem that modeled an organizational hierarchy as a tree and answered queries such as finding direct reports and everyone in a department. Clarify what “coding round” means. Ask: > Is this a LeetCode-style DSA problem, or will it involve ML coding? Will I need numpy, PyTorch, or basic Python? The DSA bar is equivalent to a software-engineering interview, which can surprise ML candidates. Use our [software-engineering course](https://www.aced.io/courses/software-engineering?ref=exponent-blog.ghost.io) and [mock interview tools](https://www.aced.io/practice?ref=exponent-blog.ghost.io) to prepare. ## Final interview loop The final loop contains five to six interviews, each lasting approximately **45 to 60 minutes**. For ML engineering roles, prepare for: - **One additional coding round:** DSA similar to the screen, but potentially more practical and domain-relevant. - **One ML coding round:** NumPy data manipulation and implementation of ML components. - **One ML system-design round:** Discussion-based and not heavily dependent on whiteboard writing. Prepare with our [system design course](https://www.aced.io/courses/system-design-interviews?ref=exponent-blog.ghost.io). - **Two behavioral rounds:** One leadership-focused project deep dive and one general behavioral interview. See our [behavioral interview course](https://www.aced.io/courses/eng-behavioral?ref=exponent-blog.ghost.io). ### Coding round A candidate received a physics-based collision-simulation problem involving diagrams. It was more practical and less predictable than a standard LeetCode problem, so clarifying the requirements was important. ### ML coding round A candidate worked with 3D trajectory data, manipulating it with NumPy to tokenize positions and predict the next step. Be fluent with multidimensional means, broadcasting, and reshaping; knowing the NumPy API thoroughly is central to this round. ML engineers may find it comfortable, while software engineers may need extra practice. ### ML system design round One candidate was asked to design a system that retrieves similar driving scenes from a massive video database—for example, finding all scenes with rainy conditions. The application is to build training datasets for autonomous-driving models by sampling relevant material from a large video collection. Treat the round as a conversation about tradeoffs: use a structured framework, keep high-level notes, and respond to the interviewer's follow-up questions. ### Leadership behavioral round This is a **45-minute** deep dive into one impactful project. Choose a project with substantial impact, long duration, technical complexity, and cross-team coordination. Explain both architecture decisions and organizational challenges. Start with a high-level overview and let the interviewer direct the depth rather than following a rigid script. ### General behavioral round Expect questions about conflict resolution, mentorship, cross-team influence, and “Why Waymo?” Be ready to discuss influencing teams that depend on your work but do not report to you. ## Waymo interview questions These are examples of real questions from candidate interview experiences. Browse more in our [Waymo interview question bank](https://www.aced.io/questions?company=waymo&ref=exponent-blog.ghost.io). ### Coding - Create a company-org-chart model and search it by names, job titles, and teams. (BFS/DFS) - Given a diagram of obstacles, determine how they will collide and what the outcome will be. - Find the sum of all binary-tree elements within a given range. - Find the container with the maximum volume of water. - Given an unsorted array, find duplicates in constant time without built-in Python functions. ### Machine learning - Use NumPy to manipulate 3D trajectory data, tokenize positions, and predict the next step. - Implement batch normalization with NumPy. - Determine when to forget outdated data, update a model, and evaluate performance. - Design a post-accident algorithm for a self-driving car. ### ML system design - Design retrieval of similar driving scenes from a massive video database, focusing on data sampling, embeddings, and similarity search. - Design an ML experiment-tracking and analysis platform. - Design a language-detection system. - Design a Google newsfeed algorithm. ### Behavioral - Walk through your most impactful project. What was the technical challenge, and how did you structure your team? - Describe a conflict with a partner team and how you resolved it. - How do you work differently with junior and senior engineers? - Describe a time you convinced someone to change their mind. - Why Waymo? ## Tips for getting hired at Waymo - **Do not underprepare for coding.** Two of the five or six final-round interviews are pure coding: DSA plus ML coding. Underpreparing for algorithms can prevent you from passing the initial screen. - **Be fluent in NumPy.** Practice multidimensional means, broadcasting, reshaping, and practical data manipulation. - **Treat ML system design as a conversation.** Explain tradeoffs using a structured framework and follow the interviewer's direction. - **Prepare one deep project story.** Choose a high-impact, long-running project involving significant technical and cross-team work. Our [mock interview tools](https://www.aced.io/practice?ref=exponent-blog.ghost.io) can help refine the story. - **Clarify every coding round.** During the recruiter prep call, ask whether each round uses DSA, ML coding, NumPy, PyTorch, or basic Python. ## Frequently asked questions ### Does Waymo hire new graduates? Yes. Waymo has internship and new-grad programs, particularly in software engineering and ML engineering. See current openings on the [Waymo early careers page](https://careers.withwaymo.com/early-careers?ref=exponent-blog.ghost.io). ### Does Waymo offer remote positions? Waymo follows Alphabet's hybrid work model. Most engineering roles are based in the Bay Area with some hybrid flexibility; depending on the role, work may be in-person, hybrid, or remote. ### Can I reapply to Waymo after rejection? Yes. Standard Alphabet reapplication policies generally allow reapplication after **6 to 12 months**, depending on the outcome. ### Is the process virtual? Virtual and in-person options are typically available. The final loop may be distributed across multiple days. ### Does Waymo provide interview feedback? Feedback is limited. Decisions generally follow a pass-or-fail format without detailed feedback. ## Prepare with Aced - Review [Waymo interview questions](https://www.aced.io/questions?company=waymo&ref=exponent-blog.ghost.io). - Practice with our [mock interview tools](https://www.aced.io/practice?ref=exponent-blog.ghost.io). - Use our [machine learning engineer course](https://www.aced.io/courses/ml-engineer?ref=exponent-blog.ghost.io). - Build system-design skills with our [system design interview course](https://www.aced.io/courses/system-design-interviews?ref=exponent-blog.ghost.io). ![Waymo company logo](https://exponent-static.s3.amazonaws.com/company_logos/waymo.png) ### Browse Waymo interview questions Review recently asked Waymo interview questions and answers from real candidates: [Browse questions](https://www.aced.io/questions?company=waymo&src=blog). ### Your Aced membership awaits We provide interview guides, insider tips, and courses for technology interviews. 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