ML Engineer, Perception Research

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 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.

This role follows a hybrid work schedule and reports to a Principal Research Scientist.

• Conduct comprehensive experimentation to train and deploy state-of-the-art Multimodal LLMs and World models to perform 3D Perception using sensor information from Camera, LiDAR and Radar..
• Partner effectively with engineering and research teams across Waymo to deploy new models, and implement efficient workflows for model development and continuous training on new front-filled data.
• Apply and develop techniques such as quantization, pruning, knowledge distillation, and efficient attention mechanisms.
• Develop and maintain scalable data pipelines for Training & Eval to process data from multiple sources.
• Design and implement evaluation frameworks for perception models.
• Develop infrastructure for large-scale model distillation and bulk-inference pipelines for teacher models.
• Experiment with different model partitioning and sharding strategies to improve scalability and efficiency.
• Build and maintain tools for performance analysis, profiling (e.g., xprof), and debugging of ML models.

• PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field, with 4+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models.
• Proficiency in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches.
• Proficiency in JAX, Flax, and potentially Tensor Flow/PyTorch.
• A willingness to work with complexity of globally distributed inference infrastructure.
• Hands on experience with optimizing the training and inference of Transformer architectures

• PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning.
• Substantial involvement in and contributions to high impact industry AI projects.
• Experience in generative models for domains such as world models, images, videos, 3D, using techniques such as diffusion or autoregressive models.
• Experience contributing to frameworks and libraries that improve training speed and scalability (e.g., JAX, Gemax, XManager)

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