CV/ML Systems Engineer, Hardware Architecture

CV/ML Systems Engineer, Hardware ArchitectureMeta 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.ResponsibilitiesDrive 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 siliconDefine 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 generationsLead hardware-software co-design initiatives by aligning software development milestones with silicon tape-out schedules, ensuring timely integration of perception algorithms onto target hardwareEstablish integration requirements, success criteria, and program milestones that translate perception algorithm demands into actionable deliverables across hardware and software teamsIdentify and resolve cross-functional dependencies, risks, and bottlenecks in software-to-hardware integration programs, driving accountability across engineering organizationsCoordinate with silicon engineering, firmware, and platform teams to define architectural requirements for custom CV/ML accelerators and SoCs based on software integration needsDevelop and maintain program tracking frameworks to evaluate integration progress, hardware readiness, and software maturity against real-world CV/ML workload requirementsEngage with external silicon partners, sensor vendors, and IP providers to align integration timelines and ensure emerging CV/ML hardware technologies are incorporated into program plansCommunicate program status, integration trade-offs, and roadmap decisions to executive leadership and cross-functional stakeholders through written proposals, program reviews, and status updatesDefine benchmarking methodologies and integration validation criteria for CV/ML software onto hardware systems across the full stack from sensor ingestion through algorithm executionQualifications12+ years of experience in hardware systems architecture with a focus on computer vision, machine learning inference, or high-performance signal processing systemsExperience 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 topologyExperience 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 profilingExperience developing system performance models and using simulation or analytical frameworks to evaluate architectural trade-offs for real-time CV/ML workloads at scaleTrack record of driving multi-year hardware architecture roadmaps and influencing silicon strategy across large engineering organizations in consumer electronics or spatial computing domains Background in collaborating with computer vision and ML research teams to translate novel perception model architectures into hardware-efficient deployment targets on custom siliconExperience architecting CV/ML inference systems for power- and area-constrained wearable or mobile devices, including VR headsets, AR glasses, or similar spatial computing platformsExperience evaluating and integrating emerging memory and sensor technologies into CV/ML system architectures, including event cameras, time-of-flight sensors, or near-sensor compute approachesFamiliarity with perception workloads such as SLAM, depth estimation, hand and eye tracking, scene segmentation, or neural radiance field rendering and their specific hardware demands