About HP

At HP, you’ll have a chance to create tools, technology, and solutions that reshape the way the world works in the future. If you’re looking to join a company that allows you to connect with a network of professionals eager to support you in doing your best work, we want to talk to you. Our legendary culture guides every employee toward success—fostering collaboration and driving innovation.

Operating in over 170 countries, HP is always creating new services, products, and capabilities giving you more opportunities to advance your career. Here, innovation is the key to professional development and career mobility.

When you join HP, you’re joining a company that believes every voice matters and that we all deserve a seat at the table. From the boardroom to factory floor, we create a culture where everyone is respected and where people can be themselves. You will be part of a global laboratory where different perspectives and experiences will help you solve problems in new ways. This is where you can build a long and wide-ranging career.

About The Role

We are looking for an AI Runtime / ML Model Systems Engineer to define the architecture and performance strategy for AI workloads across HP PCs. You will determine how software should use compute engines, memory, hardware capabilities, and different model types to deliver reliable and responsive AI experiences. Working with platform architects and engineering teams, you will shape runtime abstraction frameworks, model placement policies, and execution strategies across CPUs, GPUs, and NPUs. You will translate product and experience requirements into architectural decisions that balance inference performance, resource availability, power consumption, and thermal constraints. This role emphasizes system design, technical planning, and architectural guidance. Success requires deep knowledge of AI runtime behavior and the ability to define solutions that engineering teams can implement and validate across different hardware configurations.

What You Might Do

Define runtime architectures and performance strategies for AI workloads across heterogeneous compute resources.

Assess how model types, hardware capabilities, memory requirements, and workload behavior should influence system design.

Design runtime abstraction layers and interfaces that connect applications and models to inference engines and hardware backends.

Define model placement, scheduling, and routing policies across CPU, GPU, and NPU resources, including fallback behavior and resource availability considerations.

Evaluate inference technologies such as ONNX Runtime, TensorRT, and OpenVINO to guide framework selection and integration decisions.

Establish approaches for memory allocation, model lifecycle management, and resource coordination when multiple AI workloads execute concurrently.

Translate product requirements into performance targets, system requirements, and architectural guidance for development teams.

Define benchmarks and validation criteria; use prototype results and system measurements to assess architectural choices.

Lead design reviews and collaborate with platform, security, and hardware and software partners to resolve dependencies and guide engineering roadmaps.

Essential Qualifications

7+ years of experience in systems engineering, software architecture, AI runtimes, or a related field.

Experience designing AI runtime systems, inference platforms, or model execution frameworks for edge devices or other resource-constrained environments.

Strong understanding of how AI models use compute engines, memory, and hardware acceleration, including trade-offs across CPU, GPU, and NPU execution.

Experience with inference frameworks such as ONNX Runtime, TensorRT, or OpenVINO and an understanding of their hardware dependencies and execution behavior.

Ability to translate product and performance requirements into system architectures, interface definitions, and technical specifications.

Strong software engineering fundamentals, including APIs, concurrency, memory management, and the interaction between operating systems and hardware.