Director of Engineering, ML Infrastructure

The ML Infrastructure 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, debugging and evaluation, deployment, and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, ensuring greater degrees of consistency and repeatability, and addressing the "last mile" of getting models into production and managing them once they are in place. We work with machine learning experts in all parts of the company. This is crucial to the development of Waymo Foundation Models for driving including evaluation, auto-labeling and distillation.

In this hybrid role, you'll report to our Vice President, Engineering.

You will:

• Set the direction of multiple teams working on ML infrastructure for training, inference, and training data collection
• Communicate a long-term vision for ML Infra at Waymo in the era of Generative AI
• Translate that vision to annual operating plans and goals
• Coach managers, sr. managers, technical leads, and principal engineers to be independent leaders
• Work with customers to understand their needs and working backwards from those needs
• Look around corners, make bold bets, and be technically motivated as the team delivers big, discontinuous, creative improvements
• Set specific, measurable, achievable, and goals for teams within the organization to support Waymo's business
• Deploy scalable mechanisms to foster a culture of continuous improvement
• Dive deep and audit our systems using mechanisms like weekly operational metrics reviews, monthly goals reviews, and quarterly program reviews
• Hire and develop the best ML infra and optimization talent in the world
• Set a high bar and be divinely discontent with the status quo

You have:

• Hired and developed world class teams of 100 people including L6 and L7 managers and scientists
• Written vision documents for organizations and mapped them to multi-year roadmaps
• Created sustainable mechanisms for continuous improvement
• Built and maintained ML training pipelines at scale
• Led teams who improve ML models for speed and efficiency
• Built and operated large-scale distributed systems
• Expertise in the common ways of gathering training data for ML systems
• Expertise in the area of Generative AI, specifically when it comes to pretraining foundation models, fine tuning them, and distilling them to smaller models

We prefer:

• Can explain all aspects of foundation models at a high level
• Have built infra on which foundation models were trained

#LI-Hybrid