Job description

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. The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. You will: 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. You have: 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. We prefer: 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. 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 $298,000 — $368,000 USD

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

llmmachine learning

Platform: greenhouse
Effort: quick

Benefits

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Vision