Senior ML Compiler Engineer, Compute

Waymo's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the fully autonomous vehicle’s software stack. To achieve our mission, we architect and create high-performance custom silicon; we develop system-level compute architectures that push the boundaries of performance, power, and latency; and we collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance. We are a multidisciplinary team seeking curious and talented teammates to work on one of the world’s highest performance automotive compute platforms.

This role follows a hybrid work schedule and you will report to a Software Engineering Manager.

You will:

• Leverage agentic AI to accelerate analysis, planning and implementation of production software improvements
• Analyze the performance characteristics of code generated by our production grade compiler, and design and implement optimizations to improve that performance
• Design and implement compiler support for novel features of our high-performance architecture
• Work with hardware architects to understand and influence the development of our unique neural network inference platform through hardware/software codesign
• Work with model developers to tune their neural networks for better inference efficiency and accuracy

You have:

• BS degree in Computer Science/Electrical Engineering or equivalent experience and 5+ years of industry experience OR
• MS degree in Computer Science/Electrical Engineering and 3+ years of industry experience
• PhD degree in Computer Science/Electrical Engineering and 1+ years of industry experience
• 3+ years experience working on compilers for parallel architectures
• 1+ years experience working with ML inference or linear algebra computation
• C++ programming skills, including leveraging agentic AI acceleration

We prefer:

• Python programming experience
• Experience with compilers for neural networks, especially MLIR-based
• Knowledge of computer architectures used for neural network inference, and neural network performance characteristics
• Knowledge of the principles behind popular machine learning and neural network algorithms and applications