Principal AI Hardware Architect for On-Device Compute

Meta; Olympia (WA); On-site; USD 260,000 - 319,000; Full time

Job summary

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 on-device AI inference and training workloads across wearables, headsets, and spatial computing platforms.

You will drive the multi-year architectural roadmap, lead system-level exploration for AI accelerators, and coordinate with silicon, firmware, software, and ML teams to translate AI workloads into hardware

Qualifications

• 15+ years of experience in hardware systems architecture.
• Experience defining SoC or system-level architecture for AI inference or training workloads.
• Experience with hardware-software co-design methodologies for on-device AI workloads.
• Experience developing system performance models and using simulation or analytical frameworks.
• Track record of driving multi-year hardware architecture roadmaps and influencing silicon strategy across large engineering organizations.

Responsibilities

• Define and own the multi-year architectural roadmap for AI compute subsystems across hardware product lines.
• Lead system-level architecture exploration for AI inference and training accelerators, including memory hierarchy design and interconnect topology.
• Drive cross-functional alignment across silicon, 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.

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 on-device AI inference and training workloads across wearables, headsets, and spatial computing platforms.

You will drive the multi-year architectural roadmap, lead system-level exploration for AI accelerators, and coordinate with silicon, firmware, software, and ML teams to translate AI workloads into hardware