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, implement, and optimize large-scale continual pre-training pipelines for cutting-edge VLM foundation models.
• Conduct research and development on novel pre-training techniques, focusing on efficiently integrating new, diverse, and multimodal data streams (e.g., visual data from different sensors) into existing models.
• Develop and rigorously evaluate metrics and methodologies for measuring the performance, and transferability of continually pre-trained foundation models in the context of autonomous driving.
• Stay current with the latest advancements in large language models, vision-language models, and continual learning, and translate relevant research into production-ready systems.

You have:

• Experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models).
• Proven expertise in LLM/VLM pre-training, continual learning with large scale datasets.
• Strong coding proficiency in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch).
• Hands-on experience with model training, evaluation, and deployment in a production environment.
• Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
  
  

We prefer:

• Experience in fine-tuning foundation models for autonomous driving or robotics applications
• 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.