About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking a Senior Principal Network Engineer to help design, deploy, and optimize next‑generation AI data center networks. AI training and inference workloads require extremely high bandwidth, deterministic low latency, and zero‑packet‑loss networking environments. In this role, you will partner closely with the Network Architecture Lead to design and scale high‑performance computing (HPC) network fabrics supporting GPU clusters. You will work across hardware, networking, and AI application layers to ensure Graphcore’s large‑scale AI infrastructure operates at peak performance. The ideal candidate brings deep experience operating hyperscale or HPC data center networks and has expertise in high‑speed Ethernet fabrics, RDMA technologies, advanced automation, and telemetry systems. The Team The Data Center Network Engineering team designs and operates the high‑performance network fabrics that power Graphcore’s AI compute platforms. The team collaborates closely with hardware engineering, AI researchers, and infrastructure teams to build scalable networking environments optimized for distributed training and infe
Senior Data Center Performance Engineer - Benchmarking and Optimization
Salary not disclosed
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Role overview
Job description
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.
NVIDIA has a rapidly expanding ecosystem of data center platform designs. From single node HGX/DGX systems all the way up to large multi-node NVLink domain rack architectures. These designs have become core to NVIDIA's rapidly growing enterprise and cloud provider businesses. Each brings together the full power of NVIDIA GPUs, NVIDIA NVLink, NVIDIA InfiniBand networking, NVIDIA Grace CPUs, and a fully optimized NVIDIA AI and HPC software stack. We are searching for a highly motivated engineer to lead performance benchmarking and optimization efforts for our data center products. You will be instrumental in ensuring our data center solutions deliver industry-leading performance for accelerated computing workloads.
What you will be doing:
Design and execute comprehensive performance benchmarking strategies for our data center platforms and products
Characterize real-world AI training, inference, and HPC workloads at scale
Define, track, and report key performance indicators (throughput, latency, efficiency, scaling)
Build automation tools and frameworks for performance monitoring and analysis
Identify and analyze performance bottlenecks across compute, memory, network and storage subsystems
Work closely with architecture, hardware, software, networking, storage and customer teams to resolve performance issues
Drive performance improvements through system tuning, configuration optimization, and architectural recommendations for future generation systems
What we need to see:
M.S. or Ph.D. in Computer Science, Electrical Engineering or related field (or equivalent experience).
8+ years of experience in performance engineering or system architecture
Deep understanding of computer architecture, hardware-software interaction and computing at-scale
Strong proficiency in performance profiling tools (Linux perf, NVIDIA Nsight Systems)
Familiarity with GPU computing and parallel programming (CUDA)
Background with HPC networking technologies (InfiniBand, RoCE, NVLink)
Programming skills in Python, C++, and shell scripting. Excellent analytical and problem-solving abilities
Adaptability and passion to learn new technologies
Ability to communicate effectively and work with cross-functional global teams
Ways to stand out from the crowd:
Experience with AI/ML frameworks (PyTorch, TensorFlow, JAX). Knowledge of MPI, collective communications (NCCL), distributed training and inference. Familiarity with NVIDIA DGX, HGX platforms and other data center solutions
Familiar with containers, cloud provisioning and scheduling tools (Docker, Kubernetes, SLURM). Understanding of storage systems and I/O performance
Track record of performance optimization in production environment. Experience with AI code generation tools
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.What they are looking for
Skills & requirements
Hiring company
Nvidia
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