Senior AI-HPC Storage Engineer

Details

NVIDIA has continuously reinvented itself over two decades. Our 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. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today!

As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking fast storage solutions to enable runs of demanding deep learning, high performance computing, and computationally intensive workloads. We seek an expert to identify architectural changes and/or completely new approaches for our GPU Compute Clusters fast storage. As an expert, you will help us with the next-gen storage solutions strategic challenges we encounter with storage design for large scale, high performance workloads, evolving our private/public cloud strategy, capacity modelling, and growth planning across our global computing environment.

Responsibilities

• Research and implementation of distributed storage services.
• Design, implement an on-prem AI/HPC infrastructure supplemented with cloud computing to support the growing needs of NVIDIA.
• Design and implement scalable and efficient next-gen storage solutions tailored for data-intensive applications, optimizing performance and cost-effectiveness.
• Develop tooling to automate management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources.
• Document the general procedures and practices, perform technology evaluations, related to distributed file systems.
• Collaborate across teams to better understand developers' workflows and gather their infrastructure requirements.
• Influence and guide methodologies for building, testing, and deploying applications to ensure optimal performance and resource utilization.
• Supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows.
• Root cause analysis and suggest corrective action for problems large and small scales.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
• 8+ years of experience designing and operating large scale storage infrastructure.
• Experience analyzing and tuning performance for a variety of AI/HPC workloads.
• Experience with one or more parallel or distributed filesystems such as Lustre, GPFS is a must.
• Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting.
• Strong Experience operating services in any of the leading Cloud environment [ AWS, Azure or GCP].
• Experience with AI/HPC cluster job schedulers such as SLURM, LSF.
• In depth understating of container technologies like Docker, Enroot.
• Experience with AI/HPC workflows that use MPI.

Ways to stand out from the crowd:

• Experience with NVIDIA GPUs, Cuda Programming, NCCL and MLPerf benchmarking.
• Experience with Machine Learning and Deep Learning concepts, algorithms and models.
• Familiarity with InfiniBand with IBOP and RDMA.
• Background with Software Defined Networking and AI/HPC cluster networking.
• Familiarity with deep learning frameworks like PyTorch and TensorFlow.

NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most resourceful and talented people in the world working for us and, due to unprecedented growth, our extraordinary engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you.