AI Systems Engineer, Hardware Architecture

Meta; New York (NY); Hybrid; USD 250,000 - 380,000; Full time

Job summary

Meta Reality Labs is seeking a principal-level AI Systems Engineer to shape the hardware architecture strategy for next-generation AI-accelerated computing systems powering AR/VR products. You will define the silicon and systems roadmap for on-device AI inference and training across wearables, headsets, and spatial computing platforms, focusing on imaging systems and camera pipelines.

You will drive multi-year roadmaps for AI and imaging architectures, collaborate with ML and camera teams, and

Qualifications

• Deep experience in camera pipeline architecture and ISP design.
• Experience defining SoC or system-level architecture for AI workloads.
• Experience architecting imaging subsystems for real-time computer vision.
• Familiarity with ML compiler stacks and ISP tuning workflows.

Responsibilities

• Define hardware architecture strategy for AI-accelerated computing systems.
• Shape silicon and systems roadmap for on-device AI inference and training.
• Drive architecture of imaging systems, camera pipelines, and ISPs.
• Collaborate with ML research and camera teams to deploy hardware-efficient models.
• Evaluate memory technologies and interconnect topologies for performance.

Skills

Camera pipeline

SoC architecture

AI inference

Imaging subsystems

Hardware-software co-design

Performance modeling

Roadmap planning

RTL handoff

Job description

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. 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
• Familiarity with custom silicon development flows for imaging and AI accelerators, including architecture-to-RTL handoff, physical design constraints, and post-silicon validation
• 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
• Expertise in computational photography pipelines, HDR processing, and neural ISP architectures
• Background in collaborating with ML research and camera teams to translate novel imaging algorithms and model architectures into hardware-efficient deployment targets
• Experience evaluating and integrating emerging memory technologies (e.g., HBM, LPDDR5X, in-memory compute) into AI and imaging system architectures