Software Engineer, ML Inference, Simulation Infrastructure

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 Simulation Infrastructure team creates reliable, scalable, and cost-effective Simulation-based products that evaluate the Waymo Driver's software stack at a massive scale. We solve complex technical challenges to build services and tools for a broad range of customers Software Engineers, Product, Data Science, System Engineering, and more. So if you want to build the next generation of Simulation products and infrastructure, we'd love to hear from you!

In this hybrid role you will report to the Software Engineering Manager.

You will:

• Build and evolve ML inference infrastructure for simulations.
• Be responsible for the reliability, latency, and user experience of ML model deployment and serving.

You have:

• B.Sc. in Computer Science, or a related field, or equivalent years of experience
• 3+ years of experience C++ and/or Golang programming experience
• Experience in developing and maintaining distributed systems.

We prefer:

• Experience working with large-scale distributed inference service.
• Experience working with popular ML frameworks, TPUs and optimizing models for serving.
• Experience with distributed systems principles, including scheduling, load balancing, and fault tolerance.
• Experience working with large FAANG scale distributed systems.

#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.

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.