About the position
Waymo is an autonomous driving technology company with the mission to be the 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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states. The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver's behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. The team's work touches on all of the most exciting aspects of machine learning, including but not limited to Large Language Models (LLMs), Reinforcement Learnings (RL), and Vision understanding. In this role, you will ensure that our systems remain reliable in the face of this rapid, experiment-driven development.
Responsibilities
- Collaborate with data scientists, engineers, and other stakeholders to deploy machine learning models into production.
- Develop and maintain CI/CD pipelines for automating data generation, model training, testing, and deployment processes.
- Implement best practices for model versioning, monitoring, and governance.
- Troubleshoot and resolve issues related to model deployment and performance.
- Stay current with emerging technologies and trends in ML Ops and DevOps domains.
- Be aware of best practices used in the Alphabet stack of ML technologies (e.g. TF, JAX, Flax, Beam etc).
Requirements
- B.S. in Computer Science, Math, or equivalent real-world experience
- 5+ years building and maintaining high-scale distributed or ML inference systems
- Coding and testing skills, specifically Python/C++
- Familiarity with large-scale fleet management, testing, and deployment, e.g.: GCP/AWS/Kubernetes, Jenkins, GCS/S3, etc.
- Familiarity with large-scale MLOps tools, e.g.: tf.serving/Torchserve, Kubeflow/Sagemaker Pipelines/Vertex AI Pipelines, etc.
- Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow.
Nice-to-haves
- Previous experience deploying and supporting machine learning models for computer vision, natural language processing, or recommendation systems.
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous Company benefits program
