Technical Lead Manager, Machine Learning Platform at Waymo in Mountain View, CA

ML Platform team at Waymo provides a set of tools and technologies to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, and model development, debugging & evaluation, deployment, and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, including Perception, Behavior Prediction, Planner, Routing, Maps and Research, 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 hand in hand with machine learning experts in all parts of the company as well as our collaborators across Alphabet.

We are looking for a strong TL/TLM who is passionate about enabling machine learning at Waymo.

In this leadership role, you'll:

• Lead a small team of software engineers that design, develop, maintain and improve software
• Work closely with our users to understand their needs and address their pain points
• Act as a sounding board and technical architect for your teams, help define technical vision and strategy
• Guide all technical aspects of development, including technical requirements definition, design, implementation, unit testing, and integration
• Work closely with engineering partners and stakeholders to drive organizational alignment
• Attract, grow, and retain a diversity of talent, providing technical direction and career growth opportunities

Minimum qualifications

• Experience managing projects across multiple teams
• Experience managing, mentoring, and coaching senior engineers
• Experience building and architecting large-scale, production quality systems, especially in applied machine learning
• Passionate about self-driving technology and its potential impact on the world
• BS, MS or PhD in CS, Math, or equivalent real-world experience
• 3+ years as Tech Lead or Manager
• Technical experience in any of the following:
• 
  
• Distributed training / metrics evaluation
• Scalable ML data platform (dataset management, validation, feature engineering, versioning etc.)
• ML Frameworks (TensorFlow, Pytorch etc.) development