Staff Tech Lead, ML Data Infrastructure and Inference Platform

The ML Data Infrastructure and Inference Platform team owns the Data as well as the Data infrastructure for all ML Flywheels at Waymo. We do this via a planet scale centralized feature store for all  ML training and evaluation, the feature extraction framework as well the inference platform for generating the data via bulk and online inferences. The objective of the team is to deliver data necessary for all our onboard as well Foundation Models to make the ML Flywheel successful and in turn lead to the successful scaling of Waymo.  The team is highly collaborative and closely works with all modeling teams within Waymo.

You will:

• As a Tech Lead you’ll  build, lead and contribute to Waymo’s ML data infrastructure platform to enable planet scale ML Flywheel for all ML models at Waymo via data store and data infra ecosystem.
• Work closely with teams across Waymo both onboard & offboard Foundation models, to understand the data infra needs, data distributions, data quality, data value, freshness and onboard these flywheels onto our planet scale data store and ensure the seamless adoption and guide the development of our infra components. 
• You will closely lead, mentor and guide a team of engineers to build and onboard teams to the infrastructure efficiently.
• The team has a large surface area and scope in terms of impact, cross team collaboration and direct interaction with ML models at Waymo. 

You have:

• 6+ years of professional experience in the field of software engineering
• Experience tech leading projects with high cross team collaboration 
• Experience tech leading a team of engineers on complex projects
• Experience in programming C++ 
• Experience with building highly scalable distributed system
• Experience with ML Data and ML Flywheels

We prefer:

• Passionate about Data and building ML infra & tools
• Experience Tech leading projects, cross functional projects
• Experience with handling large datasets in the order of exabytes
• Experience building machine learning infrastructure  and model hosting / inference infrastructure
• Experience with production services with high QPS.

#LI-Hybrid