About this opportunity
Meta lists this AI Systems Engineer, Hardware Architecture opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
# AI Systems Engineer, Hardware ArchitectureRemote, US Engineering+ 3 moreApply nowMeta Reality Labs is seeking a principal-level AI Systems Engineer to define the hardware architecture strategy for next-generation AI-accelerated computing systems powering virtual and augmented reality products. In this role, you will shape the long-term silicon and systems roadmap for on-device AI inference and training workloads across wearables, headsets, and spatial computing platforms. You will drive architectural decisions that span custom silicon, memory subsystems, interconnects, and software-hardware co-design, ensuring Meta's AI hardware remains at the forefront of performance, efficiency, and capability for immersive computing experiences.---## AI Systems Engineer, Hardware Architecture Responsibilities* Define and own the multi-year architectural roadmap for AI compute subsystems across Meta's hardware product lines, including wearables and spatial computing devices* Lead system-level architecture exploration for AI inference and training accelerators, including memory hierarchy design, interconnect topology, and power-performance-area tradeoffs* Drive cross-functional alignment across silicon engineering, firmware, software, and product teams to translate AI workload requirements into hardware specifications* Develop and maintain architectural models, performance simulators, and analytical frameworks to evaluate design tradeoffs at the system level* Identify and resolve architectural bottlenecks across the AI compute stack, from neural network operators to silicon microarchitecture* Establish technical direction for AI hardware platform decisions, including custom silicon versus third-party IP evaluation and integration strategies* Partner with machine learning researchers and compiler teams to co-design hardware-software interfaces that maximize AI model efficiency on constrained wearable platforms
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Who can apply
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