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Develops audio and vision machine learning models and core tools for deployment on edge AI devices and custom hardware accelerators. Responsibilities include prototyping research approaches, optimizing latency, memory bandwidth, data flow, and hardware utilization, collaborating with modeling and embedded software teams, delivering production solutions, and troubleshooting field issues.

Supports development, training, evaluation, pruning, and deployment validation of keyword spotting and audio event detection models for ultra-low-power edge hardware. Works on audio feature extraction, data curation, hard-negative mining, augmentation, false-accept diagnostics, model efficiency, quantization, and experiment analysis while collaborating with ML, DSP, and hardware/firmware engineers.

Lead a global Applications Engineering team supporting MEMS microphones and vibration sensors for edge AI products. Serve as primary technical resource for customers and sales, define sensor product roadmaps with Product and R&D, produce demos/reference designs/documentation, drive customer success, and collaborate cross-functionally. Travel up to 25%.

Responsible for designing digital systems for MEMS microphones, performing RTL design, supervising digital verification, and developing low-power architectures.