The opportunity
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to…
What you'll do
Design and implement an automated semantic extraction pipeline that parses: large-scale simulation logs, vehicle trajectory data, and multi-agent behavioral events into structured graph representations (entities, temporal relationships, and causal interactions)
Develop temporal graph modeling techniques to capture time-varying: multi-agent interactions, road context, and safety-critical driving events
Build a multi-modal hybrid retrieval engine (combining dense vector: embeddings, keyword search, and graph traversal) to enable fast scenario discovery and serve as structured memory for automated failure analysis agents
Create interactive data exploration tools and visualization dashboards (e.g.,: Jupyter/Colab-based explorers) to help autonomy engineers and researchers analyze complex scenario distribution
Benchmark retrieval precision, recall, and query latency against traditional: tabular and relational search baselines
Collaborate cross-functionally with simulation researchers, machine learning: engineers, and software infrastructure teams to document system architecture and establish roadmap recommendations
What they're looking for
- Currently enrolled in a graduate program (PhD or Master’s) in Computer: Science, Artificial Intelligence, Robotics, Electrical Engineering, or a related quantitative field, with at least one academic term remaining
- Strong software development experience in Python and/or C++ in a Linux development environment
- Solid foundation in core computer science concepts, data structures,: algorithm complexity, and distributed data systems
- Hands-on experience with modern deep learning frameworks (such as PyTorch, JAX, or TensorFlow)
