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Foster City, California, United States
Onsite
As a machine learning engineer within the Attributes team in the Perception department, you will take ownership of developing and enhancing sophisticated behavioral models for various road users, including vehicles, pedestrians, and cyclists. Your work will focus on creating and maintaining robust perception attribute models that generate critical signals for our autonomous driving stack. These signals are essential inputs that enable our Prediction and Planning teams to make intelligent, safe driving decisions for our autonomous vehicles.
Create and maintain perception attribute models that generate essential signals, enabling our autonomous vehicles to understand and predict the behavior of various road users.
You will collaborate closely with Prediction and Planning teams to optimize your models' outputs, directly influencing how our autonomous vehicles make real-time driving decisions.
Work with data labeling and ontology teams on data labeling and ontology definitions of the road users in different attributes and generate auto-labeling or data mining strategies for different attributes.
You will help shape the future of autonomous mobility by bridging the critical gap between raw perception data and autonomous decision-making.
MS/PhD in computer science or related fields with a minimum of 7 years of relevant experience
Experience with training and deploying Deep Learning models
Experience with knowledge distillations from large foundation models
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
Fluency programming in Python and extensive experience with algorithm design
Strong mathematics skills
Familiarity of VLMs/VLAs/ViTs
Experience with large model distillation in a production environment
Familiarity with C++