I applied online. The process took 2 months.

The entire process consisted of four interviews (45 minutes each, video interview). The virtual onsite is composed of 2 LC, 1 ML design, and 1 ML basic.

The first was a casual chat with a research scientist. He introduced his work and the general research direction at Waymo, mentioning that Waymo recruits research interns with a focus on publishing papers, etc. He then asked about my background/papers and might follow up with some details. The interview atmosphere was very relaxed, and we didn't delve too deeply; I'm not entirely sure what the purpose was.

The second was a coding interview. My question was: Given a 2D trajectory, use a series of (x, y, t) tuples to represent the position at different times, where t monotonically increases, and the trajectory between two points is a straight line.

• 1. The first question was relatively simple: given any t, find the coordinates (x, y).
• 1. After completing the first question, he followed up by asking: given any point on a plane, find the shortest path between it and this trajectory. If the trajectory has many segments, he asked how to reduce the computation cost.

The third was a research interview. I also talked to a research scientist, but this time it was much more serious. He spent about half the time asking questions based on my paper, covering everything from motivation and high-level ideas to specific implementations—basically, anything that came to mind. The other half consisted of some basic machine learning questions. I recall:

• 1. Explain the principles of dropout / how it's used during training or testing / why it reduces overfit, and provide explanations from different perspectives.
• 1. What are overfit / underfit, and how are they determined?
• 1. I can't remember exactly, but it might have involved ensemble.