Director - Agentic AI Computing Foundation

HP•Spring, TX

2d•$182,750 - $320,250

About The Position

Lead the engineering organization that builds the shared backbone for executable agentic AI on HP PCs and edge devices. Own platform capabilities spanning APIs, security, hardware interfaces, data access, and policy frameworks. Make agentic features portable, reliable, and efficient across supported device configurations while providing clear technical contracts to feature teams. The organization advances HP’s AI roadmap through capabilities deeply integrated into HP PCs and edge devices. The Director sets the platform foundation roadmap, leads device integration and execution-efficiency work, establishes security and control capabilities, partners with engineering teams, enables feature squads, and builds a global systems and AI engineering leadership team. The role owns platform investment, technical talent development, capacity, and technical debt while balancing shared needs across Divisions. Success is measured by meeting target-device latency, memory, energy-use, and stability budgets; adoption of shared APIs and improved integration lead times; security, policy, and recovery results; field incident trends; predictable platform commitments; and sustained engineering capability.

Requirements

Typically 15 or more years in systems, embedded, device-platform, or AI engineering.
At least 7 years leading teams, including experience managing managers and senior architects.
A record of delivering production capabilities integrated into PCs or comparable edge devices, including responsibility for runtime reliability, OS or hardware interfaces, and field behavior.
Deep understanding of heterogeneous compute and on-device inference, including CPU, GPU, NPU, memory hierarchy, performance profiling, and power or thermal constraints.
Strong systems architecture experience involving native software, concurrency, interprocess communication, API compatibility, device services, and secure lifecycle management.
Practical experience enabling agentic or generative AI systems, with understanding of orchestration, tool permissions, model/runtime integration, observability, and evaluation.
Ability to lead global technical organizations and partner engineering.
Degree in computer science, electrical or computer engineering, or a related discipline, or equivalent relevant experience.

Nice To Haves

Depth in Windows device platforms and familiarity with supported Linux or embedded environments, firmware interfaces, silicon SDKs, and hardware-in-the-loop testing.
Experience with model compression, quantization, inference acceleration, local context stores, and policy-based placement across device and cloud.
Experience with OEM or ODM development, enterprise manageability, protocol interoperability, and platform adoption by internal developers or ISVs.

Responsibilities

Set the foundation roadmap for context, identity, capability discovery, orchestration, and manageability; define stable APIs and versioned contracts reusable across experiences.
Lead collaboration across runtime, OS services, drivers, firmware, and hardware interfaces, and establish lifecycle behavior for startup, sleep and resume, connectivity changes, updates, and recovery.
Guide inference and workload placement across CPU, GPU, NPU, and cloud resources; set performance budgets for latency, memory, power, and thermals with hardware and silicon partners.
Provide trusted identity, permission boundaries, policy enforcement, protected data access, and auditable agent actions; support safe tool invocation, user control, and enterprise endpoint management.
Work with Vali’s team and other engineering stakeholders to make features executable; integrate internal capabilities and relevant industry protocols through tested adapters and contracts.
Commit Foundation engineers to agile squads from discovery through adoption; deliver reusable increments, compatibility tests, telemetry, and reliability improvements alongside feature development.
Build a global systems and AI engineering leadership team; own platform investment, technical talent development, capacity, and technical debt while balancing shared needs across Divisions.
Meet agreed platform budgets for target-device latency, memory, energy use, and stability.
Increase feature adoption of shared APIs, improve contract-test pass rates, and reduce integration lead time.
Improve policy and security validation, recovery behavior, and field incident trends; deliver predictable platform commitments and reduce blocked feature dependencies.