AI Systems Engineer, Hardware Architecture

About The Position

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.

Requirements

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

Nice To Haves

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

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