Develop multimodal large language models for autonomous vehicles, optimizing performance and safety through advanced machine learning techniques and real-world data validation.

The Operations Lead will manage the Perception Data team, overseeing data collection and labeling for AI models, ensuring high quality and timely delivery while working with ML engineers and external partners.

As a Senior/Staff Software Engineer, you will improve robotic driving behaviors through algorithm development, testing, and performance reviews while ensuring safety and innovation.

Lead the development of machine learning algorithms focusing on perception for autonomous driving, collaborating across teams and mentoring ML developers.

The Software Engineer will develop algorithms and code related to AI, robotics, and sensing technologies, impacting key systems at Zoox.

Develop multimodal large language models for urban driving environments, implement ML solutions, and validate algorithms using real-world data.

The role involves developing and optimizing large language models for robotaxis, enhancing their understanding of urban environments, and integrating models into decision-making processes.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Contribute to 3D occupancy and segmentation networks development, optimizing deep learning models for real-time inference using multi-modal data fusion.

The Machine Learning Engineer Intern at Zoox will work on perception systems, developing algorithms for autonomous driving, collaborating with teams, and leveraging advanced machine learning models and datasets.

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Lead data-quality and labeling efforts for perception ML: implement scalable quality measures, drive data-centric model improvements, maintain ontology/class structure, evaluate human vs auto-labeling trade-offs, and build metrics for labeling throughput and annotator/vendor quality.

Lead full-cycle technical recruiting for AI and software roles in Boston. Source, screen, and evaluate candidates, partner with hiring managers and leadership, manage offers, and continuously improve candidate experience and recruiting processes.