AI and HPC Systems Performance Engineer

AI and HPC Systems Performance Engineer

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week 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

High Performance Computing, AI and Labs is a critical element of HPE. We are focused on delivering innovative solutions that accelerate our customers’ digital transformation, enabling them to tackle their complex, and data-intensive workloads. Combining deep expertise and the development of the world’s most cutting-edge, high-performance supercomputers, is defining the next era of computing delivering valuable insight & innovation. Join us and redefine what’s next for you.

We are looking for an experienced AI Performance Engineer with expertise in tuning GPU server performance for a variety of Artificial Intelligence (AI) training and inference workloads running on Linux platforms. The ideal candidate will be a senior or principal-level engineer with demonstrated experience installing, configuring, characterizing, and optimizing industry-standard server infrastructure including compute, storage, networking, and accelerator technologies for AI workloads.

The individual in this role will investigate workload behavior by capturing and analyzing system telemetry, profiling data, traces, and performance metrics to characterize workload execution and identify opportunities for optimization through software, firmware, and hardware configuration changes. They will work closely with customers, partners, and internal engineering teams to optimize performance and scalability of AI solutions deployed on HPE platforms.

This role requires strong research, analytical, and problem-solving skills, along with experience building, deploying, optimizing, and maintaining containerized AI/ML environments and workloads. The engineer will collaborate with software development teams to capture workload telemetry, improve observability, and optimize AI software stacks running on HPE infrastructure.

They should understand the performance and capacity characteristics of modern AI training and inference workloads and be comfortable working independently to evaluate emerging technologies, author technical papers, and develop AI reference architectures.

Experience troubleshooting complex, multi-tier software systems and distributed AI environments is highly desirable. Experience with one or more of PyTorch, JAX, Hugging Face Transformers, DeepSpeed, Megatron-LM, Ray, vLLM, SGLang, TensorRT-LLM, Dynamo, ONNX Runtime, Kubernetes, Redis, Vector Databases, Retrieval-Augmented Generation (RAG) architectures, distributed training and inference, and large-scale AI/LLM workloads is highly desired.

The ideal candidate will also have experience characterizing and optimizing performance across multi-GPU and distributed AI environments utilizing modern GPU interconnect, networking, and storage technologies.

Strong written and verbal communication skills are required.

What you’ll do:

• Install, configure, and optimize complex AI infrastructure components including GPU servers, storage systems, high-speed networking, and AI software stacks.

• Develop automation scripts, deployment frameworks, and Infrastructure-as-Code solutions to streamline AI platform provisioning and workload execution.

• Perform system-level performance characterization and optimization of AI training and inference workloads on HPE platforms utilizing GPU accelerators and distributed computing technologies.

• Design, execute, and analyze performance benchmarks for AI/ML workloads, including large language models (LLMs), multimodal models, Retrieval-Augmented Generation (RAG) pipelines, and distributed training environments.

• Characterize and optimize performance across multi-GPU and distributed AI environments using modern interconnect, storage, and networking technologies such as InfiniBand, Ethernet fabric, GPUDirect, and RDMA.

• Capture, analyze, and interpret system telemetry, performance metrics, logs, traces, and profiling data to identify bottlenecks and optimization opportunities.

• Develop tools, software, and automation frameworks to improve AI workload observability, performance analysis, scalability testing, and benchmark execution.

• Collaborate with customers, partners, and internal engineering organizations to characterize, troubleshoot, and optimize AI solutions deployed on HPE infrastructure.

• Work closely with ISV, IHV, GPU vendor, and open-source ecosystem partners to evaluate, optimize, and validate AI software and hardware solutions.

• Evaluate emerging AI frameworks, models, accelerators, and infrastructure technologies; provide technical recommendations and performance guidance.

• Author technical reports, white papers, reference architectures, benchmark studies, and best-practice guidance for AI performance optimization and solution design.

• Document findings, performance issues, and optimization recommendations, and communicate technical results to engineering teams, customers, and management.

• Provide technical leadership, mentoring, and guidance to junior engineers and contribute to the development of performance engineering best practices.

• Communicate project status, technical risks, and performance findings to management and stakeholders in a timely manner.

What you need to bring

• Typically 8+ years of experience

• Strong experience with Linux system administration and command-line environments across multiple enterprise Linux distributions.

• Experience with modern AI/ML frameworks and ecosystems including PyTorch, JAX, Hugging Face Transformers, and related technologies.

• Experience with AI model training, inference, benchmarking, performance characterization, and optimization.

• Experience with data analysis, statistical methods, experiment design, and performance modeling techniques.

• Experience conducting technical research and evaluating emerging AI technologies, frameworks, and hardware platforms.

• Experience with high-performance networking technologies including InfiniBand, RDMA, RoCE, and Mellanox/NVIDIA networking solutions.

• Strong analytical, troubleshooting, and root-cause analysis skills.

• Proficiency in one or more programming or scripting languages such as Python, Bash, Go, C++, or similar.

• Experience working with complex, distributed, multi-layer software systems and AI infrastructure stacks.

• Experience using performance profiling, tracing, observability, and benchmarking tools to analyze system and application performance.

• Experience with containerized and orchestrated environments including Docker, Kubernetes, and related cloud-native technologies.

• Experience analyzing and optimizing AI workloads running on GPU-accelerated systems.

• Experience with distributed training and inference frameworks and large-scale AI/LLM workloads.

• Experience with GPU accelerator technologies, memory hierarchies, and AI software stacks including CUDA, NCCL, and related ecosystem tools.

• Experience with distributed GPU environments and multi-node AI clusters.

• Experience with LLM serving frameworks such as vLLM, TensorRT-LLM, SGLang, or similar technologies.

• Aptitude for self-learning; Learns new concepts quickly

• Excellent written and verba