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Job Summary

The Commercial Systems Software Engineering organization is developing HP's Memory Management platform, a foundational component of the Agentic AI Software Stack designed to optimize memory utilization, model execution, and resource orchestration for next-generation AI PCs.

The Senior Software Engineer will define the architecture and technical strategy for AI memory management across heterogeneous compute environments, including CPUs, GPUs, NPUs, storage, and cloud resources. The role will focus on enabling efficient execution of large language models, multi-agent workloads, multimodal AI applications, and enterprise AI scenarios through intelligent memory allocation, model residency management, KV cache optimization, workload prioritization, and Quality of Service (QoS) controls.

This position requires deep expertise in operating systems, memory architectures, AI runtime systems, workload scheduling, performance optimization, and large-scale software architecture. The architect will work closely with silicon vendors, operating system partners, AI framework teams, and product organizations to develop innovative technologies that maximize AI performance while maintaining system responsiveness, security, and power efficiency.

Responsibilities

Define and drive the end-to-end architecture for HP's Memory Management platform.

Architect systems for intelligent memory allocation, prioritization, and optimization across AI workloads running on client devices.

Lead design of model residency management capabilities to enable efficient deployment and execution of large AI models on resource-constrained systems.

Define policies for KV cache management, context window optimization, model sharing, and memory multiplexing across multiple AI agents.

Develop policy engines that dynamically balance performance, memory consumption, power usage, and user responsiveness.

Architect workload QoS and resource arbitration mechanisms across CPU, GPU, NPU, memory, storage, and network resources.

Define strategies for local, hybrid, and cloud model execution based on system state, workload requirements, and resource availability.

Lead development of telemetry and analytics systems that monitor memory pressure, model utilization, resource contention, and workload efficiency.

Partner with operating system, firmware, silicon, and AI framework teams to optimize memory behavior across the full platform stack.

Establish technical roadmaps, engineering standards, and architectural guidance across multiple software teams.

Mentor senior engineers and architects while influencing company-wide AI platform strategy.

Engage with ecosystem partners including Microsoft, Intel, AMD, Qualcomm, NVIDIA, and ISVs to align memory optimization technologies and industry standards.

Education & Experience Recommended

Four-year or Graduate Degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or related technical field, or equivalent experience.