Machine Learning Engineer, Runtime & Optimization

The ML Platform team at Waymo provides a set of tools to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, model development, optimization and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, including Perception, Planner, Research and Simulation.

We are looking for engineers with ML software or ML systems expertise to help us improve compute performance on both cloud and car. You'll work across the entire ML stack from the system perspective, from efficient deep learning models, model compression, ML software (e.g. JAX, XLA, Triton, and CUDA), to . You will be pleasantly challenged with deploying Waymo ML models on limited computation resources. In this hybrid role, you will report to the Senior Manager of Runtime and Optimization.

You will:

• Lead the collaboration with the world-class Waymo ML scientists in perception, planner, research and simulation. Identify opportunities in both systems and models to make ML workloads faster.
• Lead projects from proposals through execution by developing junior engineers.
• Analyze and improve ML system workloads on both cloud and self-driving cars .
• Apply model optimization, efficient deep learning techniques and ML software improvements to Waymo's ML systems.

You have:

• M.S. in CS, EE, Deep Learning or a related field
• 2+ years of experience as a technical lead, including writing project plans, engaging with customer teams, mentoring, responsible for goals & execution, reporting status.
• 5+ years of experience developing solutions in ML systems or ML software stack (Pytorch/JAX/TF, runtime libraries, ML compiler).
• Deep understanding of ML system architecture, performance analysis and tools.
• Strong Python or C++ programming skills

We prefer you have one or more of the following:

• PhD in CS, EE, Deep Learning or a related field.
• Familiarity with the HW architecture of ML hardware accelerators (e.g., GPU/TPU).
• Deep knowledge of model optimization or efficient deep learning techniques for foundation models or LLM.
• Experience with GPU HW or TPU HW and related system software.