Everything we know about interviewing at Waymo: the process stage by stage, what each round tests, compensation by level, and reports from candidates who interviewed.
What the process looks like, and what Waymo is really testing for.
Waymo runs a multi-step hiring loop that mixes recruiter screens, coding and technical assessments, and multiple rounds of onsite or virtual onsite interviews that also include behavioral evaluation. Across roles, the process repeatedly measures both execution, meaning coding and data work, and communication, meaning how you explain your reasoning and handle uncertainty.
The topics that show up most in question data are Data Analysis (percentile 95), Python (90), Statistical Analysis (76), and then Machine Learning (77) plus C++ (77). You also see Stakeholder Communication (72) and Data Visualization (69), with Cloud Computing (30) and Deep Learning (54) appearing more selectively. Algorithms (36) is present but not the dominant theme compared to data and analysis.
Expect a loop that can feel tightly packed and sometimes inconsistent in execution quality across interviewers, based on candidate reports. The aggregated difficulty distribution is mostly medium (64.8%), with easy (16.8%), hard (16.0%), and very hard (1.6%), and the overall offer rate across reports is 0.4%, so you should focus on being consistently strong across the core data, coding, and explanation dimensions rather than betting on one perfect round.
The strongest signal in the data is that Data Analysis and Statistical Analysis lead the topic mix, so you should prepare to explain your data reasoning clearly, not just produce correct code.
6 stages, based on 445 candidate reports.
You start with an initial discussion with a recruiter to assess your background and alignment with open roles. Some screens explicitly include fit for the Machine Learning Engineer role, along with your background and research interests.
You may complete a written questionnaire with basic kinematics, logic, and behavioral questions. This step is used to assess basic qualifications and fit for the role.
You get a technical phone interview focused on coding skills and basic embedded concepts, with the possibility of a collaborative editor coding question. Depending on the team focus, it may include high-level ML theory discussion.
You may take a hands-on assessment focused on SQL proficiency and basic data manipulation. Some reports also mention practical writing exercises to demonstrate technical writing skills.
You complete a loop of multiple rounds, typically 4 to 5 interviews, mixing coding, system design, machine learning fundamentals, and behavioral assessments. Candidate reports also describe days or sessions that feel back-to-back and sometimes include stakeholder communication style evaluation.
After interviews, a hiring committee holistically reviews performance, followed by a final decision-making step based on evaluations. You may also meet multiple stakeholders as part of the overall process before the committee review.
How often each skill shows up across reported interview loops.
Each guide has the questions Waymo interviewers actually ask, the loop structure, and total compensation by level.
Estimated total compensation: base salary plus stock and annual cash bonus.
Patterns from candidates who got offers, and the mistakes that most often sink a loop.
Read what candidates said about interviewing at Waymo: the loop, difficulty, and outcomes, straight from recent reports for each role.
Answered from real candidate and workplace data, marked up for rich results.
Verbatim snippets pulled from employee and candidate reviews.
Exciting technology but demands can lead to burnout.
Waymo offers a decent work environment for those passionate about cutting-edge technology.
The long hours and high expectations can be challenging.
Be prepared for demanding work hours and a fast-paced environment.
The team culture is strong, and there are ample opportunities for advancement.
Waymo offers a great culture with significant advancement opportunities, but the work environment can be unstable.
Companies that hire for the same data roles, with the same depth of prep.
