Staff Machine Learning and Modeling Researcher

This individual leads, plans, synthesizes ambiguous or conflicting requirements and performs the complex responsibility of conducting fundamental machine learning research to create new models or new training methods in various technology areas, e.g. deep generative models, Bayesian deep learning, equivariant CNNs, Bayesian optimizations, reinforcement learning, unsupervised learning, and graph NNs. Drives systems innovations for model efficiency advancement on device as well as in the cloud. This includes auto-ML methods (model-based, sampling based, back-propagation based) for the creation and optimization of efficient models (e.g., model compression, quantization, architecture search, and kernel/graph compiler/scheduling) with or without systems-hardware co-design. Designs and tests methods for Federated Learning that balance performance with privacy and security requirements. Creates new innovative machine learning methodology for advanced uses cases to achieve performance beyond the state-of-the-art. Designs and partners with implementers of demonstration and proof-of-concept systems to validate concepts and enhance communications of complex ideas. Development of and publication of research findings in the form of presentations and conference papers may also be required. Acts as a strong contributor at design reviews and project meetings and communicates and implements a development plan. *Telecommuting may be permitted.*