Machine Learning Engineer, GPU Kernel and Runtime
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Overview
Waymo is seeking a Machine Learning Engineer to develop GPU kernels and optimize ML models for autonomous driving technology. Join a team focused on building efficient ML software stacks and enhancing performance across various platforms. 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 Waymo ML Infrastructure team accelerates Waymo’s mission, by building the best ecosystem for sustainably innovating and shipping ML powered intelligence.
Responsibilities
- •Collaborate with ML practitioners on models for perception, behavior prediction, and planning.
- •Deep dive into the NVIDIA ML software and runtime stack.
- •Analyze numeric behaviors, debug complex compiler issues, and ensure inference results are stable and consistent.
- •Develop tools/system software for optimal resource usage, hardware efficiency, and platform reliability in an ML serving system.
- •Analyze ML workload performance at the hardware level.
- •Build tools to benchmark, profile GPU execution, and productize deep learning models.
Conditions
- •Salary Range: $213,000 — $263,000 USD.
- •Eligible for Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program.
Requirements
- •B.S. or M.S. in CS, EE, Deep Learning or a related field.
- •5+ years of industry experience on system performance, hardware-level GPU optimization, or ML compilers.
- •Strong C++ and CUDA programming skills.
- •Extensive experience in NVIDIA GPU Kernel development.
- •Proven debugging and optimization experience on the XLA:GPU compiler.
- •Passion for developing and optimizing ML software stacks for modern ML accelerator architectures.