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 self-driving car, 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. Waymo interns work with leaders in the industry on projects that deliver significant impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skillset. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! This internship will be based on-site at our headquarters in Mountain View, CA. You will: * Improve signal quality in large scale perception evaluation workflows using generative AI (LLM/VLM) * Expand the capabilities of discovery evaluation system to cover a broader range of perception signals * Contribute to design and development of frameworks and methods for evaluating multi-task perception models * Collaborate with ML and infra engineers to integrate new signals to end-to-end model launch evaluation * Evaluate the impact of improvements on evaluation quality, focusing on precision, recall, and reliability You have: * Proficiency with coding in Python or C++ and navigating large codebases * Experience with building and/or evaluating machine learning models * Experience with data analysis and statistical evaluation * Enrolled in a Masters or PhD program in Computer Science, Robotics, or a similar technical field of study We prefer: * Multi-year C++ experience with contributions to large code bases or open source projects * Familiarity with perception models, in particular with object generation and semantics tasks * Experience in utilizing AI-powered workflows for data analysis or model evaluation * Experience in applying machine learning concepts and evaluation methodologies * Proven experience in building data and evaluation pipelines and processing large-scale datasets Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly Masters Pay $70—$70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85—$85 USD

2027 Summer Intern, MS/PhD, Perception, Evaluation

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