Lead innovative research and development at the intersection of machine learning, signal processing, and embedded systems for future health and input technologies across Google’s consumer hardware portfolio. Architect and train multimodal machine learning and foundation models tailored for time-series sensor data, while actively optimizing them for real-time inference on resource-constrained embedded systems. Drive hardware-software co-design by partnering tightly with Specialized Hardware Engineers, ensuring that ML architectures and algorithmic requirements directly influence next-generation sensor definitions and edge-compute selection (e.g., MCUs, DSPs, micro-NPUs).
Define the long-term technical strategy spanning cloud-based ML training infrastructure down to on-device evaluation, guiding large-scale data collection pipelines and embedded software architecture. Lead cross-functional integration efforts with Hardware, Firmware, Product, and Clinical teams to deploy highly optimized ML models and sensing algorithms into consumer-facing edge devices quickly and broadly.
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