Staff Machine Learning Engineer, Acceleration (San Francisco)

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.

Waymo has successfully deployed self-driving cars in real-world environments; now, our imperative is to scale this capability. Scale is driven by large models and data, and we are moving to ever-larger models which generalize by being trained on more data. To achieve this, we're focused on optimizing model inference and training, ensuring these advancements gracefully generalize across multiple platforms.

In this role, you'll work embedded in an ML Engineering and Modeling team, working hand-in-hand to drive scale and multi-platform support of models. This role requires to follow the latest developments in efficient ML and bring those innovations to Waymo's production systems.

In this hybrid role, you'll report to a Technical Lead Manager.

You will :

• Optimize neural model architectures and systems for high performance on multiple GPU and TPU platforms (e.g., onboard vs simulation platform)
• Optimize neural model performance and overall system performance for systems with hard real-time constraints (Waymo's onboard system)
• Develop post-training algorithms (e.g., quantization), low-level optimizations (e.g., kernel optimization), etc. for improving inference speed and reducing inference memory consumption on modern GPU and TPU architectures
• Develop new neural model architectures (e.g., sparse architectures), decoding strategies (e.g., speculative decoding), etc. for improving inference performance on modern GPU and TPU architectures
• Optimize model training speed and efficiency for large models (often memory bound) and for fine-tuning (often i / o bound)
• Collaborate with ML infra teams (inference frameworks, training frameworks), Onboard hardware and Simulation teams, and Alphabet's research teams

You have :

We prefer :

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.

Salary Range $238,000$302,000 USD