Staff Software Engineer/ Tech Lead - Onboard Model Consolidation

The Architecture & ML Integration (AMI) team sits within SIML and focuses on horizontal, cross-cutting challenges spanning Waymo's end-to-end system. Our mission is to enable rapid evolution towards an ML-first architecture that is modular, flexible, and easy to experiment with. We do this by improving onboard software architecture, system debuggability, and developer experience through better API designs and comprehensive pipelines.

The AMI team drives the convergence of Planner and Perception systems and tackles critical shared infrastructure components

- Have 8+ years of professional software development experience on large scale products

- Lead the software architecture and infrastructure development for onboard model consolidation across many onboard subsystems.

- Design and build an onboard framework to support flexible and efficient model integration and inference in Waymo's onboard and simulation environments.

- Spearhead the alignment and unification of model's data generation subsystem between model training data preparation and model inference.

- Develop Simulation solutions to enable robust testing and efficient evaluation/validation to enable good developer experience for consolidated model development.

- Embedded deeply with model teams to drive the productionization of unified models.

- Establish new software development best practices and technologies to accelerate model development and releases.

- BS/MS in Comp Sci, EE, Robotics, Physics, Math, or related field (or equivalent experience)

- Extensive experience designing and building large-scale, high-performance C++ software architecture for mission-critical systems or ML inference.

- Proven track record of leading complex, cross-functional engineering projects as a technical lead.

- Deep understanding of ML deployment, inference frameworks, and associated developer tooling.

- Good communication and collaboration skills working with cross-org partner teams

- Master's or PhD in Computer Science, Machine Learning, Robotics, or a related field.

- Experience with data pipelines and feature extraction for machine learning models.

- Prior experience in the autonomous driving industry, particularly focusing on onboard compute, perception, or planning systems.

- Experience with hardware accelerators (e.g., TPUs, GPUs) and ML models inference in edge/onboard environments.