About the job
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.
Responsibilities
Design VLM/LLM model architecture and drive strong alignment between model architectures and hardware architectures.
Optimize model performance for on-device use cases (memory, power, compute constrained environments).
Engage directly with research, software engineering, hardware engineering, and product teams to deliver end-to-end solutions.
Qualifications
Minimum
7+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models).
Proven expertise in low-latency on-device inference techniques and a deep understanding of hardware acceleration.
Extensive experience with deep learning frameworks (e.g. PyTorch, JAX) and large-scale model training.
A track record of operating effectively under ambiguity, setting direction amid rapidly evolving research and technical constraints
Experience applying large language models or foundation models in complex, safety-critical domains (e.g., autonomy, robotics, or other high-reliability systems)
Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
Preferred
Familiarity with large-scale data curation and quality assurance processes for multimodal datasets.
Background in autonomous vehicle perception, motion planning, or decision-making systems.
Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV).
PhD in a relevant field.