Meta 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. A core focus of this role is driving the architecture of advanced imaging systems and camera pipelines that enable real-time perception, scene understanding, and mixed reality experiences. You will drive architectural decisions that span custom silicon, memory subsystems, interconnects, image signal processors (ISPs), camera subsystems, and software-hardware co-design, ensuring Meta's AI hardware remains at the forefront of performance, efficiency, and capability for immersive computing experiences.

Responsibilities

Define multi-generation hardware architecture strategy for AI inference and training systems across VR, AR, and wearable device platforms
• Lead system-level architectural exploration and trade-off analysis across compute, memory hierarchy, interconnect fabric, and power delivery for on-device AI workloads
• Architect end-to-end camera and imaging pipelines, including sensor interfaces, ISP integration, and real-time image processing for computer vision and perception applications
• Drive hardware-software co-design initiatives by partnering with silicon engineering, firmware, camera systems, and ML platform teams to optimize end-to-end AI and imaging pipeline performance
• Define architectural requirements for camera subsystems including multi-camera synchronization, depth sensing, and low-latency visual processing for AR/VR applications
• Establish architectural requirements and performance targets for custom AI accelerators, ISPs, SoCs, and supporting subsystems in spatial computing devices
• Develop and maintain system performance models and simulation frameworks to evaluate architectural decisions against real-world AI and imaging workload characteristics
• Provide architectural guidance and technical direction across hardware engineering organizations, aligning imaging and AI roadmaps with product and research priorities
• Identify and resolve system-level bottlenecks in imaging latency, AI inference throughput, and energy efficiency for wearable and headset form factors
• Engage with external silicon partners, camera module vendors, and research institutions to evaluate emerging imaging technologies and incorporate them into long-range architecture plans
• Communicate architectural vision and technical rationale to executive leadership and cross-functional stakeholders through written proposals and design reviews

Minimum Qualifications
• 12+ years of experience in hardware systems architecture, with a focus on AI, ML, imaging systems, or high-performance compute systems
• Deep expertise in camera pipeline architecture, including image signal processing (ISP), sensor integration, and end-to-end imaging system design
• Experience defining SoC or system-level architecture for AI inference or training workloads, including memory subsystem design, compute hierarchy, and interconnect topology
• Experience architecting imaging subsystems for real-time computer vision applications, including multi-camera systems, depth sensing, and visual-inertial odometry
• Experience with hardware-software co-design methodologies for on-device AI and imaging workloads, including familiarity with ML compiler stacks and ISP tuning workflows
• Experience developing system performance models and using simulation or analytical frameworks to evaluate architectural trade-offs at scale
• Track record of driving multi-year hardware architecture roadmaps for imaging and AI systems and influencing silicon strategy across large engineering organizations

Preferred Qualifications
• Expertise in computational photography pipelines, HDR processing, and neural ISP architectures
• Experience architecting AI and imaging systems for power- and area-constrained wearable or mobile devices, including VR headsets, AR glasses, or similar spatial computing platforms
• Experience evaluating and integrating emerging memory technologies (e.g., HBM, LPDDR5X, in-memory compute) into AI and imaging system architectures
• Background in collaborating with ML research and camera teams to translate novel imaging algorithms and model architectures into hardware-efficient deployment targets
• Familiarity with custom silicon development flows for imaging and AI accelerators, including architecture-to-RTL handoff, physical design constraints, and post-silicon validation