Senior Software Engineer, Bulk/Interactive Inference

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 DriverTM—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.

We’re looking for a software engineer to join the team to build and maintain the critical data and ML pipelines that powers ML development at Waymo.

In this hybrid role, you will report to the Head of ML Platform- Senior Staff Software Engineer.

You will:

• Develop Waymo's inference platform to make it scalable, high throughput, and low latency
• Work closely with other teams across Waymo in hosting both internal and external ML models, including LLMs
• Improving the efficiency of running inference on these large models to increase throughput and save cost
• Deploy and integrate model inference solutions across a variety of use cases, such as distillation, eval, dataset generation, active learning, and auto-labeling

You have:

• 5+ years of professional experience in the field of software engineering
• Experience in programming C++
• Experience with building highly scalable distributed system

We prefer:

• Passionate about building internal infra and tools
• Experience with building model hosting and inference solutions
• Experience with handling datasets in the order of exabytes

#LI-Hybrid