Performance Engineering Architect
This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.
Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
Job Description:
In the HPE Hybrid Cloud, we lead the innovation agenda and technology roadmap for all of HPE. This includes managing the design, development, and product portfolio of our next-generation cloud platform, Green Lake. Working with customers, we help them reimagine their information technology needs to deliver a simple, consumable solution that helps them drive their business results. Join us redefine what’s next for you.
What you’ll do:
We are seeking a Performance Expert to join our Performance Engineering team. The engineer will characterize and optimize the performance of AI solutions across GPUs, servers, storage, networking, system software, and AI frameworks.
The role combines hands-on benchmarking, system-level analysis, workload optimization, automation, and collaboration with engineering, product management, field, sizing, and partner teams. The engineer will turn product features, emerging technologies, and customer questions into reproducible performance evidence that supports product design, release readiness, solution sizing, and customer decisions.
What you need to bring:
Design and execute performance studies for generative AI, machine learning, and other AI workloads.
Characterize inference and training performance across models, GPUs, precision formats, deployment profiles, and scale configurations.
Measure latency, throughput, time to first token, inter-token latency, concurrency, scaling efficiency, resource utilization, and cost/performance.
Evaluate single-GPU, multi-GPU, and multi-node configurations.
Analyze GPU communication, host-to-GPU bandwidth, PCIe topology, NUMA placement, memory bandwidth, storage, and networking behavior.
Characterize LLM serving, retrieval-augmented generation, vector database, object detection, speech, multimodal, and agentic AI workloads.
Develop custom benchmark suites and automation when standard benchmarks do not represent the required workload.
Identify performance bottlenecks and recommend changes to hardware configuration, BIOS settings, operating systems, drivers, runtimes, frameworks, and applications.
Establish repeatable test methodologies, baselines, acceptance criteria, and release-performance gates.
Automate benchmark execution, telemetry collection, result processing, comparison, and reporting.
Produce clear performance reports, sizing evidence, engineering recommendations, and field-facing guidance.
Evaluate emerging AI technologies, models, GPU platforms, storage architectures, and distributed inference techniques.
Collaborate with solution engineering, product management, quality engineering, storage, compute, networking, solution-sizing, field, and technology-partner teams.
Mandatory skills: PCAI, GenAI, and AI workloads
Strong understanding of generative AI and machine learning performance concepts , particularly inference performance.
Hands-on experience deploying and benchmarking AI or GenAI workloads on GPU-accelerated systems.
Understanding of key inference metrics like End-to-end latency, Time to first token, Inter-token latency, Token throughput, Request throughput, Concurrency, Batch size, GPU utilization…