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Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
The ML Ops team, part of Waymo ML Platform team, builds tools and infrastructure to realize the ML flywheel at Waymo. This includes building automation and orchestration solutions to make complex ML workflows manageable and reliable. This team also partners closely with the modeling team to realize solutions to speed up developer velocity.
You will:
- Develop and contribute to Waymo's data infrastructure platform to enable plant scale ML Flywheel at Waymo for all ML models via data store and data infra ecosystem.
- Work closely with teams across Waymo both onboard & offboard ML models, including LLMs to understand the data needs, data distributions, data quality, data value, freshness and onboard these flywheels onto our planet scale data store.
- Improve the efficiency of data storage, data sharing across models.
- Deploy and integrate data solutions across a variety of use cases, such as distillation, eval, dataset generation, active learning, and auto-labeling across all Waymo ML models
You have:
- 4+ years of professional experience in the field of software engineering
- Experience programming in C++
- Experience with building highly scalable distributed systems
We prefer:
- Passionate about building ML infra and tools
- Experience 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
The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.Â
Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.Â
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