Senior Machine Learning Engineer, Waymo

• 5+ years of 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

Desirable

• 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
• 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