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
Designing and Building Machine Learning Models: Writing clean, high-performance code to implement core algorithms for entity-centric lane geometry detection and relational topology decoding (e.g., merges, splits, predecessor/successor connectivity).
Running Experiments and Training Pipelines: Setting up data pipelines and training neural networks across vehicle sensor modalities and map priors, leveraging techniques like proxy auto-encoding and prior-dropout to handle real-world challenges like construction zones and occlusions.
Benchmarking and Analyzing Performance: Creating structured evaluation metrics to benchmark model accuracy and topological correctness across complex intersections, analyzing failure cases, and iterating on architectural designs.
Cross-Functional Collaboration: Partnering closely with research mentors, buddy, and upstream/downstream engineering teams to evaluate downstream planning impact and package insights for publication or internal deployment.
Currently pursuing a Master's or PhD in Computer Science, Robotics,: Electrical Engineering, Machine Learning, or a related quantitative discipline.
Strong programming proficiency in Python and solid experience with modern: deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
What they're looking for
- Hands-on experience designing, training, and debugging deep learning: architectures for Computer Vision, 3D Perception, or Graph Neural Networks (e.g., Transformers, DETR-based detectors, GNNs, or BEV perception).
- Solid foundational knowledge of 2D/3D geometry, coordinate transformations, and spatial/relational reasoning.
- Track record of publications in top-tier conferences in machine learning,: computer vision, or robotics (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, ICRA, CoRL, AAAI).
- Experience with vectorized HD map learning, lane topology estimation, or: dynamic roadgraph modeling (e.g., MapTR, TopoNet, LaneGAP, or similar architectures).
