Lead Edge AI Engineer
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La posizione
Lead the development and refinement of Hydranet-based multi-task learning models and video action recognition for edge devices using PyTorch.
Drive end-to-end edge deployment on embedded Android platforms, integrating with Qualcomm SNPE/QNN DSP workflows.
Optimize models for resource-constrained environments by reducing power consumption, managing thermal constraints, and minimizing latency during model switching.
Ensure system stability with safe graph reconfiguration and robust runtime management for dynamic model loading/unloading.
Collaborate with Firmware and Mobile teams to fuse signals for informed decision-making and real-time localization tasks.
Provide technical leadership, mentor team members, and communicate complex data insights to cross-functional stakeholders.
Drive end-to-end edge deployment on embedded Android platforms, integrating with Qualcomm SNPE/QNN DSP workflows.
Optimize models for resource-constrained environments by reducing power consumption, managing thermal constraints, and minimizing latency during model switching.
Ensure system stability with safe graph reconfiguration and robust runtime management for dynamic model loading/unloading.
Collaborate with Firmware and Mobile teams to fuse signals for informed decision-making and real-time localization tasks.
Provide technical leadership, mentor team members, and communicate complex data insights to cross-functional stakeholders.
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