CV/ML Systems Engineer, Hardware Architecture

Meta Reality Labs is seeking a principal-level CV/ML Systems Engineer to lead the integration of software programs onto hardware systems across the organization. In this role, you will drive end-to-end software integration programs spanning computer vision and machine learning workloads onto custom silicon, sensor subsystems, and hardware platforms powering next-generation virtual and augmented reality products. You will operate at the intersection of program leadership and technical architecture, orchestrating cross-functional efforts to deliver CV/ML capabilities onto hardware with the performance, efficiency, and latency required for immersive, real-time spatial computing experiences.

Responsibilities

• Drive organization-wide software integration programs onto hardware platforms, orchestrating cross-functional teams across silicon engineering, ML platform, perception algorithms, and hardware systems to deliver CV/ML capabilities on custom silicon
• Define and own the end-to-end program roadmap for integrating CV/ML inference pipelines onto VR headsets, AR glasses, and wearable spatial computing platforms across multiple product generations
• Lead hardware-software co-design initiatives by aligning software development milestones with silicon tape-out schedules, ensuring timely integration of perception algorithms onto target hardware
• Establish integration requirements, success criteria, and program milestones that translate perception algorithm demands into actionable deliverables across hardware and software teams
• Identify and resolve cross-functional dependencies, risks, and bottlenecks in software-to-hardware integration programs, driving accountability across engineering organizations
• Coordinate with silicon engineering, firmware, and platform teams to define architectural requirements for custom CV/ML accelerators and SoCs based on software integration needs
• Develop and maintain program tracking frameworks to evaluate integration progress, hardware readiness, and software maturity against real-world CV/ML workload requirements
• Engage with external silicon partners, sensor vendors, and IP providers to align integration timelines and ensure emerging CV/ML hardware technologies are incorporated into program plans
• Communicate program status, integration trade-offs, and roadmap decisions to executive leadership and cross-functional stakeholders through written proposals, program reviews, and status updates
• Define benchmarking methodologies and integration validation criteria for CV/ML software onto hardware systems across the full stack from sensor ingestion through algorithm execution

Minimum Qualifications

• 12+ years of experience in hardware systems architecture with a focus on computer vision, machine learning inference, or high-performance signal processing systems
• Experience defining SoC or system-level architecture for CV or ML inference workloads, including image signal processor pipelines, compute hierarchy design, memory subsystem architecture, and interconnect topology
• Experience with hardware-software co-design methodologies for on-device CV/ML workloads, including familiarity with ML compiler stacks, operator fusion, quantization impacts on hardware design, and perception algorithm profiling
• Experience developing system performance models and using simulation or analytical frameworks to evaluate architectural trade-offs for real-time CV/ML workloads at scale
• Track record of driving multi-year hardware architecture roadmaps and influencing silicon strategy across large engineering organizations in consumer electronics or spatial computing domains

Preferred Qualifications

• Background in collaborating with computer vision and ML research teams to translate novel perception model architectures into hardware-efficient deployment targets on custom silicon
• Experience architecting CV/ML inference 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 and sensor technologies into CV/ML system architectures, including event cameras, time-of-flight sensors, or near-sensor compute approaches
• Familiarity with perception workloads such as SLAM, depth estimation, hand and eye tracking, scene segmentation, or neural radiance field rendering and their specific hardware demands