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 mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: Research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using AV sensor data Design and implement post-training recipes—including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)—to improve perception generalization in long-tail autonomous driving scenarios Conduct large-scale experiments and ablation studies on autonomous driving benchmarks, with the opportunity to publish findings at top-tier AI/CV conferences (e.g., CVPR/ICCV/ECCV/NeurIPS/ICLR/ICML) You have: Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field Hands-on experience & strong publications in perception, and/or LLM/VLM Knowledge of pre-training and post-training of the large foundation models Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) We prefer: Experience with visual reasoning for LLM/VLM/robotics/driving Experience with RL training in LLM/VLM/robotics Experience with open-vocabulary perception 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 PhD Pay $85 — $85 USD

Requirements

Must-haves

Research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using AV sensor data
Design and implement post-training recipes—including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)—to improve perception generalization in long-tail autonomous driving scenarios
Conduct large-scale experiments and ablation studies on autonomous driving benchmarks, with the opportunity to publish findings at top-tier AI/CV conferences (e.g., CVPR/ICCV/ECCV/NeurIPS/ICLR/ICML)
Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field
Hands-on experience & strong publications in perception, and/or LLM/VLM
Knowledge of pre-training and post-training of the large foundation models
Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)
Experience with visual reasoning for LLM/VLM/robotics/driving
Experience with RL training in LLM/VLM/robotics
Experience with open-vocabulary perception

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningllmpython

Platform: greenhouse
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