NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.
Come work for the team that brought to you NCCL, NVSHMEM & GPUDirect. Our GPU communication libraries are crucial for scaling Deep Learning and HPC applications! We are looking for a motivated Performance engineer to influence the roadmap of our communication libraries. The DL and HPC applications of today have a huge compute demand and run on scales which go up to tens of thousands of GPUs. The GPUs are connected with high-speed interconnects (eg. NVLink, PCIe) within a node and with high-speed networking (eg. Infiniband, Ethernet) across the nodes. Communication performance between the GPUs has a direct impact on the end-to-end application performance; and the stakes are even higher at huge scales! This is an outstanding opportunity for someone with HPC and performance background to advance the state of the art in this space. Are you ready for to contribute to the development of innovative technologies and help realize NVIDIA's vision?
What you will be doing:Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack
Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available
Triage and root-cause performance issues reported by our customers
Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information
Collaborate with a very dynamic team across multiple time zones
M.S. (or equivalent experience) or PHD in Computer Science, or related field with relevant performance engineering and HPC experience
3+ yrs of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM)
Experience conducting performance benchmarking and triage on large scale HPC clusters
Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
Implement micro-benchmarks in C/C++, read and modify the code base when required
Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python
Familiar with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker)
Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones
Practical experience with Infiniband/Ethernet networks in areas like RDMA, topologies, congestion control
Experience debugging network issues in large scale deployments
Familiarity with CUDA programming and/or GPUs
Experience with Deep Learning Frameworks such PyTorch, TensorFlow
NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
