
Waymo Software Engineer candidates report recruiter screening followed by coding, design, and behavioral or manager conversations. Expect live implementation, graph and grid reasoning, edge cases, and clear trade-off discussion.
$202K
Avg. Base Comp
$317K
Avg. Total Comp
3 rounds
Typical Rounds
2-4 weeks
Process Length
Waymo Software Engineer interviews reported here span recruiter screening, technical screens, and virtual loops. Live coding with clear reasoning is the recurring theme. Candidates describe graph traversal, BFS/DFS pathfinding, grid or matrix problems, calculator-style implementations, and follow-ups that change constraints or test edge cases. Some prompts use driving or autonomous-vehicle framing, but the reported work remains practical software problem solving rather than a requirement to know a particular vehicle stack.
One successful candidate described runnable-code expectations: working in an IDE, producing executable code, setting up classes, adding unit tests, and accounting for edge cases. Other accounts emphasize proactively explaining the approach and its trade-offs, especially when an interviewer provides limited guidance.
Design and discussion rounds can be substantial. One report described preparing for low-level design focused on modular, extensible code and system-design follow-ups about database choices and write bottlenecks. Another candidate encountered an ML system-design scenario involving a vehicle at a pedestrian crosswalk. Behavioral and manager conversations may probe recent projects, technologies used, architectural decisions, and individual contributions. Some reports also include ML-, robotics-, or autonomy-related technical discussion. Exact coverage varies by team and level.
Synthesized from 11 candidate reports by our editorial team.
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Real interview reports from people who went through the Waymo process.
The most noticeable part of the Waymo interview was that the coding work felt closer to a real application scenario than a standard LeetCode prompt. I was expected to drive the discussion myself, especially with interviewers who were fairly quiet, rather than waiting for hints or a highly interactive back-and-forth. A key expectation was explaining the tradeoffs in my solution: I needed to state the pros and cons, not just arrive at working code.
The technical questions were described as medium-to-hard in difficulty, with emphasis on practical problem solving. Grid traversal and simulation-style problems seemed particularly relevant, such as navigating a car through a 2D grid with circular obstacles or tracing a ball through a grid with diagonal barriers. Other examples included implementing a basic calculator and maintaining segment-based average speeds while supporting add, remove, and query operations. These questions test BFS/DFS, interval handling, grid traversal, and the ability to structure an implementation cleanly under interview conditions.
I also found it important to know the standard function names in the language I was using, since that was an expected baseline. Overall, I would go in prepared to communicate proactively, explain why one approach is preferable to another, and treat the coding round as an applied engineering problem rather than relying only on memorized LeetCode patterns. I did not receive an offer.
Prep tip from this candidate
Practice grid traversal and simulation problems such as obstacle navigation and ball-drop paths, plus stateful interval-style operations like add/remove/query. During every solution, explicitly discuss tradeoffs and proactively narrate your reasoning, since quiet interviewers may not prompt you.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation, sometimes followed quickly by scheduling. It may cover past projects, role interests, and an overview of later technical stages; one report explicitly described this screen as about 30 minutes.
Candidates report a technical screen that may be 45 minutes and use live coding. Reported topics include graph traversal, BFS/DFS, DP, greedy problems, a history buffer, matrix or grid tasks, and occasionally a Python data-wrangling exercise. Expect edge cases, complexity discussion, and possible follow-ups.
Candidates report multi-round virtual loops with additional coding and system-design interviews. Coding may use autonomous-driving scenarios while testing general graph, pathfinding, state tracking, or implementation skills. Design discussions may cover product-scale or infrastructure systems, scalability, data flow, and trade-offs.
Candidates report manager, behavioral, or leadership conversations that examine recent projects, technologies used, architectural decisions, and individual contributions. Some loops also include ML, robotics, or autonomy-related technical discussion, depending on the team.