Tech Deep DiveOnsiteSoftware Engineer, Machine Learning EngineerLast reported November 2025Low Frequency
Problem Overview
Given multimodal sensor data (LiDAR, radar, etc.) and camera data from an autonomous vehicle, design one or multiple ML agents/systems to: (1) predict surrounding environment changes over time, and (2) anticipate and warn of impending dangers. The design should cover model selection, data processing pipeline, model architecture details, loss function design, training strategy/stages, and analysis of emergency/edge-case scenarios an autonomous vehicle might encounter.
Follow-up Prompts
Interviewers escalate the problem with these extensions. Be prepared to discuss each one.
01How do you design the loss function for predicting environment changes or danger events, especially given class imbalance between normal and dangerous situations? (when: After initial model proposal)
02How do you handle the multi-modal fusion of sensor (LiDAR/radar) and camera data? (when: After model architecture discussion)
03What training stages or curriculum would you use to train the model effectively? (when: After fusion and architecture discussion)
04Brainstorm emergency or edge-case scenarios an autonomous vehicle might encounter — how does your system handle them? (when: After training strategy discussion)
05Would you use a single agent or multiple specialized agents? What are the tradeoffs? (when: If single agent proposed)
Waymo Focus
**Alternative approaches:** End-to-end imitation learning (Simpler pipeline by learning directly from expert demonstrations; however, harder to interpret, less generalizable to rare emergency scenarios, and requires large amounts of expert-labeled data.); Rule-based / model-based danger prediction (Highly interpretable and reliable for known scenarios, but does not generalize well to novel or complex environments and requires extensive manual rule engineering.); Reinforcement learning agent (Can learn optimal policies in simulation; difficult to train safely in real environments, requires careful reward shaping, and may struggle with rare real-world edge cases.)
Waymo · Tech Deep Dive · Reported 2× across candidate reports