Scope, lead, and execute foundational research in learning, uncertainty estimation, and automated system diagnosis to enable robust, adaptive perception and decision-making stacks. Develop novel methodologies for out-of-distribution (OOD) detection, active learning with demonstrable impact on behavior and semantics, multimodal retrieval, epistemic/aleatoric uncertainty quantification, and sensor-to-perception-to-action stack calibration.
Partner with research leads, software engineers, and product teams to integrate breakthroughs into scalable, (and possibly real-time) autonomous infrastructure. Author high-impact papers in premier machine learning, computer vision, and robotics venues (e.g., CVPR, ECCV, NeurIPS, ICRA, IROS), contributing code, models, and datasets where applicable.
Job description provided by Google.
View the original posting
