Description
We're looking for a Senior AI/HPC Engineer to join our infrastructure Specialist team. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.
Primary responsibilities will include deploying, managing and maintaining AI/HPC infrastructure in Linux-based environments for new and existing customers. You'll be the domain expert with customers during planning calls through implementation, handover-related documentation and perform knowledge transfers required to support customers as they begin rolling out some of the most sophisticated systems in the world!
To succeed in this role, you'll need:
- BS/MS/PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields.
- 5+ years providing in-depth support and deployment services, solving problems for hardware and software products.
- Knowledge and experience with Linux System Administration, process management, package management, task scheduling, kernel management, boot procedures/troubleshooting, performance reporting/optimization/logging, network routing/advanced networking (tuning and monitoring).
- Cluster management technologies.
- Scripting proficiency.
- Good interpersonal skills with the ability to maintain and deliver resolutions for customer-blocking issues as they arise. Excellent verbal and written English skills.
- Strong organizational skills and ability to prioritize/multi-task easily with limited supervision.
- Industry-standard Linux certifications.
- Experience with Schedulers such as SLURM, LSF, UGE, etc.
If you have demonstrated hands-on experience with MPI (e.g., OpenMPI, MPICH), proficient in distributed communication programming and cluster debugging, in-depth understanding of NCCL principles and applications, with expertise in collective communication optimization for NVIDIA GPU clusters, experience in deploying and optimizing high-speed networks (InfiniBand/Ethernet), with a clear understanding of how network architecture impacts GPU cluster performance, familiarity with automation tools (Ansible, Salt, Puppet, etc.), capable of implementing batch configuration and operational automation for GPU clusters and LLM deployment environments, knowledge and hands-on experience with Kubernetes, including container orchestration for AI/ML workloads, resource scheduling, scaling, and integration with HPC environments, then we'd love to hear from you!