Staff Software Engineer / Tech Lead (Onboard Model Consolidation)

Join Waymo, the leading autonomous driving technology company. As a Staff Software Engineer / Tech Lead, you will lead the software architecture and infrastructure development for onboard model consolidation across various subsystems. You will design and build an onboard framework to support flexible and efficient model integration and inference, and develop simulation solutions for robust testing and evaluation. You will also establish new software development best practices and technologies to accelerate model development and releases. Enjoy a comprehensive benefits package, including medical, dental, and vision insurance, competitive compensation, and a hybrid work model.

• 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.
• Have 8+ years of professional software development experience on large scale products
• BS/MS in Comp Sci, EE, Robotics, Physics, Math, or related field (or equivalent experience)
• Deep understanding of ML deployment, inference frameworks, and associated developer tooling
• 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
• Good communication and collaboration skills working with cross-org partner teams
• Experience with data pipelines and feature extraction for machine learning models
• Master's or PhD in Computer Science, Machine Learning, Robotics, or a related field
• 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