Waymo Software Engineer interviews combine coding, often graph or grid problems, with system design and some ML or autonomous-driving questions. Candidates describe a recruiter screen, technical rounds and a behavioral conversation.
$201K
Avg. Base Comp
$366K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Waymo's software engineer interviews as coding that leans on graph and grid traversal. Several report BFS/DFS or pathfinding problems, such as finding a path from point A to B on a map, checking whether a path exists in a 2D matrix with an added twist, or a bi-directional BFS question. Others mention a recursive 2D matrix problem, a greedy algorithm question and a C++ history buffer.
Coding is not always a pure algorithms exercise. One candidate received a Python data-wrangling task on CoderPad with prewritten classes and little explanation. Another said the interviewer focused heavily on edge cases and expected correct, compilable code. A third noted that a long graph question was hard to finish in a 45-minute slot. Clarify vague prompts early and aim for a complete solution rather than only the main idea.
Many loops go beyond generic software engineering. One candidate had two robotics and autonomous-driving rounds and an ML system design round on predicting whether a car should stop or go at a crosswalk from sensor inputs. The same candidate was also asked about Vision Transformers. Others report a high-level system design round, an ML question in a final video interview, and an introductory conversation about computer vision models such as YOLO. Behavioral rounds range from a casual manager chat to a tech-leadership interview with five to six questions.
Most reports here ended without an offer. Loop structure and recruiter communication differed from person to person, so confirm each stage's format with your recruiter.
Synthesized from 21 candidate reports by our editorial team.
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Real interview reports from people who went through the Waymo process.
The process kicked off with a recruiter screening to discuss the role and scope, which felt pretty standard. After that, I moved into the technical rounds, and things got serious fast. I had two coding interviews back-to-back, each testing LeetCode-style problems at a medium-to-hard difficulty. The first one was about graph search and shortest path problems, and what made it tricky was that it wasn't a straightforward problem you'd find on LeetCode — the interviewer started with a simpler version and kept layering on harder follow-ups, so I had to think on my feet and optimize as we went. The second coding round had a similar feel, progressive and building in complexity.
Then came the system design rounds, which were two separate interviews focused on machine learning systems — one diving into online AV systems and the other offline. These weren't typical system design questions; they were specialized to Waymo's autonomous vehicle work, so you really needed to think about how ML pipelines and inference systems work at scale. There was also a behavioral round where they dug into my past experience in enterprise environments, asking about how I'd handled complex projects and cross-team collaboration.
What surprised me was how comprehensive and rigid the whole thing felt. The interviewers were professional and clear about what each round would cover, and they gave me time for questions at the end of each session. But there were pretty long delays between rounds, and the communication from the recruiting team felt a bit disorganized at times. The whole process spanned multiple days with the technical portion taking a solid two hours in one sitting — a theoretical hour followed by a practical coding hour. By the end, I felt like they were thorough in evaluating both depth and breadth, but the experience also highlighted how specialized the skillset needs to be for this role.
Prep tip from this candidate
Focus on progressive LeetCode medium-to-hard graph and shortest path problems where follow-ups layer in complexity rather than memorizing specific problems. More importantly, brush up on ML system design specific to autonomous vehicles — understand the architecture of online inference systems and offline training pipelines, as that's where their system design rounds diverge from typical tech company interviews.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Waymo
Write a function to return the value of the nearest node that is a parent to both nodes.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates typically report a recruiter conversation covering background, past projects and the loop ahead, about 30 minutes in one account. Recruiters may explain that later rounds include coding, ML or domain topics, and a behavioral conversation, though the exact mix appears to vary by team.
Candidates report a coding screen of about 45 minutes. Reported problems include a hard recursive 2D matrix problem, bi-directional BFS, a C++ history buffer and a Python data-wrangling task on CoderPad. Some interviewers began with resume and project discussion. Practice narrating your approach and reaching a working solution early.
Virtual coding rounds commonly involve graph traversal and pathfinding, such as a modified 2D matrix path question, along with greedy problems and C++ fundamentals. Interviewers reportedly push on edge cases and complete, compilable solutions. Some interviewers are less interactive, so you may need to drive the conversation and ask clarifying questions.
Reports mention a high-level system design round and an ML system design question on whether a car should stop or go at a crosswalk. Some candidates also met robotics, autonomous-driving, ML or Vision Transformer questions. The mix may depend on the team and level, so ask the recruiter which themes to prepare.
Candidates report behavioral or technical-leadership interviews covering past experience and fit. One described five to six behavioral questions, while another had a more casual manager chat with technical elements.
