Job Summary:
NVIDIA has been transforming computer graphics and accelerated computing for over 25 years, now focusing on AI to define the next era of computing. As a Senior HPC Storage Engineer, you will lead the research, design, and implementation of innovative storage solutions for high performance computing workloads, while collaborating with teams to optimize infrastructure performance and resource utilization.
Responsibilities:
• Research and analyze existing internal distributed storage services.
• Research, design, and implement scalable, next-gen distributed storage services for HPC workloads, optimizing both performance and cost-effectiveness to meet NVIDIA’s growing infrastructure needs
• Develop tooling to automate management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources.
• Detail the general procedures and practices, perform technology evaluations, related to distributed file systems.
• Collaborate across teams to better understand developers' workflows and capture their infrastructure requirements.
• Influence and guide methodologies for building, testing, and deploying applications to ensure efficient 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
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
• 8+ years of experience designing and/or operating large scale storage infrastructure.
• Experience analyzing and tuning storage performance for a variety of workloads.
• Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting
• In depth understanding of container technologies like Docker, Enroot
Preferred:
• Extensive experience with parallel and distributed filesystems (Ceph, Weka.io, Vast, Lustre, GPFS) and Linux storage kernel development.
• Proficient with NVIDIA GPUs, CUDA programming, and NCCL, including performance benchmarking via MLPerf.
• Deep familiarity with storage hardware (HDDs, SSDs, NVMe), enclosures, and specialized appliances like Network Appliance.
• Strong background in Software Defined Networking (SDN) and high-performance networking for AI/HPC clusters.
• Practical experience applying industry-standard frameworks, specifically PyTorch and TensorFlow.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.