Job description

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. 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 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 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

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Keywords to mirror

mlmachine learning

Platform: meta
Effort: medium

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