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.
You will:
- Design and improve distributed input data pipelines for large-scale machine learning training workloads.
- Collaborate with researchers and machine learning engineers to resolve bottlenecks in data pipeline performance.
- Improve runtime goodput of training workloads by optimizing input data processing systems for scalability and reliability.
- Implement and maintain advanced machine learning infrastructure tools including ML Pathways, Grain, JAX, and TensorFlow.
- Evaluate and integrate modern technologies to enhance the performance and scalability of machine learning systems.
- Promote best practices for distributed systems architecture and contribute to technical leadership within the team.
You have:
- Bachelor's degree in Computer Science, Mathematics, or a related field, and 5 years of progressive, post-baccalaureate experience in the job offered or in a Software Engineering-related occupation.
Position requires:
- 5 years of experience in Machine learning frameworks and distributed data processing libraries, including TensorFlow, JAX, ML Pathways, Apache Beam, etc.;
- 5 years of experience in Python or C++ for machine learning libraries and distributed systems;
- 2 years of experience in Technical leadership in architecting ML infrastructure for large foundation models (LLMs);
- 2 years of experience in Productionizing, evaluating, serving large foundation models in multi-host distributed ML runtimes; and
- 2 years of experience in Optimizing computational efficiency (duty cycles, memory efficiency) of a distributed ML training or inference.
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.