Nvidia is looking for a Senior Site Reliability Engineer, AI Infrastructure to provide leadership in the design and implementation of groundbreaking GPU compute clusters for deep learning, HPC, and other intensive workloads. You will identify architectural changes and new approaches to address strategic challenges in compute, networking, storage, and growth planning.
What You'll Do
- Provide leadership and strategic guidance on the management of large-scale HPC systems including the deployment of compute, networking, and storage.
- Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutions.
- Build and maintain AI and ML heterogeneous clusters on-premises and in the cloud.
- Create and cultivate customer and cross-team relationships to reliably sustain the clusters and meet evolving user needs.
- Support our researchers to run their workloads including performance analysis and optimizations.
- Conduct root cause analysis and suggest corrective action.
- Proactively find and fix issues before they occur.
What We're Looking For
- Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
- Minimum 8 years of experience designing and operating large scale compute infrastructure.
- Experience with AI/HPC advanced job schedulers, such as Slurm, K8s, RTDA or LSF.
- Proficient in administering Centos/RHEL and/or Ubuntu Linux distributions.
- Solid understanding of cluster configuration management tools such as Ansible, Puppet, Salt.
- In depth understanding of container technologies like Docker, Singularity, Podman, Shifter, Charliecloud.
- Proficiency in Python programming and bash scripting.
- Applied experience with AI/HPC workflows that use MPI.
- Experience analyzing and tuning performance for a variety of AI/HPC workloads.
- Passion for continual learning and staying ahead of emerging technologies and effective approaches in the HPC and AI/ML infrastructure fields.
Nice to Have
- Background 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.
- Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads.
- Familiarity with deep learning frameworks like PyTorch and TensorFlow.
Technical Stack
- Slurm, Kubernetes, RTDA, LSF
- Centos/RHEL, Ubuntu
- Ansible, Puppet, Salt
- Docker, Singularity, Podman, Shifter, Charliecloud
- Python, bash, MPI
- NVIDIA GPUs, CUDA, NCCL
- InfiniBand
- Lustre, GPFS
- PyTorch, TensorFlow
Team & Environment
Member of the GPU AI/HPC Infrastructure team.
Benefits & Compensation
- Highly competitive salaries
- Comprehensive benefits package
- Equity
- Compensation: $184,000 USD - $356,500 USD + equity: Eligible
NVIDIA is committed to fostering a diverse 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.
