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
Own ML features for Scene Understanding (traffic controls, signs, special: regions such as RRX and flood zones, lane states) end to end: data → model → eval → onboard launch
Mine and curate large-scale driving data (real and simulated) to find: long-tail failures, build training and eval sets, and design labeling and supervision
Design, train, and iterate on model tasks and heads in large multi-task: models. Improve calibration, robustness, and generalization to new geographies
Build rigorous evaluation: PRT metrics, slicing taxonomies, side-by-sides, and staging launches, and turn the results into launch decisions
Integrate models into the onboard stack (C++), combining learned outputs with: safe heuristic fallbacks, Mendel-guarded rollouts, and map-contradiction handling
Triage real driving events, find the root cause anywhere in the stack, and: propose surgical or systemic fixes. Work closely with Perception, Reasoning, Mapping, and ML Infrastructure teams
What they're looking for
- BS/MS/PhD in CS, EE, Robotics, ML, or a related field, or equivalent practical experience.
- Hands-on experience shipping ML models to production, including training,: offline evaluation, and deployment (L4: 2+ yrs; L5: 4+ yrs, or a PhD plus 2+ yrs)
- Strong data mining / data engineering: building large-scale batch pipelines (Beam/Flume/Spark or similar) and analytical SQL to find, slice, and curate data
- Proficiency in Python with a modern DL framework (JAX, TensorFlow, or PyTorch) and production C++
