NVIDIA is the leader in AI, machine learning and datacenter acceleration. NVIDIA is expanding that leadership into datacenter networking with ethernet switches, NICs and DPUs 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 tackle, and that matter to the world. This is our life's work, to amplify human imagination and intelligence. Make the choice, join our diverse team today!
Want more jobs like this?
Get Data and Analytics jobs delivered to your inbox every week.
As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking GPU compute clusters that powers all AI research across NVIDIA. We seek an expert to build and operate these clusters at high reliability, efficiency, and performance and drive foundational improvements and automation to improve researchers productivity. As a Site Reliability Engineer, you will help us with the strategic challenges we encounter including: compute, networking, and storage design for large scale, high performance workloads, effective resource utilization in a heterogeneous compute environment, evolving our private/public cloud strategy, capacity modeling, and growth planning across our global computing environment and building automation and tooling.
What you'll be doing:
In this role you will be building and improving our ecosystem around GPU-accelerated computing including developing large scale automation solutions. You will also be maintaining and building deep learning AI-HPC GPU clusters at scale and supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows. You will design, implement and support operational and reliability aspects of large scale distributed systems with focus on performance at scale, real time monitoring, logging and alerting. Additional responsibilities include:
- Design and implement state-of-the-art GPU compute clusters
- Optimize cluster operations for maximum reliability, efficiency, and performance
- Drive foundational improvements and automation to enhance researcher productivity
- Tackle strategic challenges in large-scale, high-performance computing environments
- Troubleshoot, diagnose and root cause of system failures and isolate the components/failure scenarios while working with internal & external partners
- Building automation for AI-HPC GPU Cluster bring up and scaled up operation
- Write and review code, develop documentation and capacity plans, debug the hardest problems, live, on some of the largest and most complex systems in the world
- Implement remediations across software and hardware stack according to plan, while keeping a thorough procedural record and data log.
What we need to see:
- Bachelor's degree in Computer Science, Electrical Engineering or related field or equivalent experience with a minimum 5 years of experience designing and operating large scale compute infrastructure
- Proven experience in site reliability engineering for high-performance computing environments with operational experience of at least 5K GPUs cluster.
- Deep understanding of GPU computing and AI infrastructure
- Passion for solving complex technical challenges and optimizing system performance
- Experience with AI/HPC advanced job schedulers, and ideally familiarity with schedulers such as Slurm
- Working knowledge of cluster configuration management tools such as BCM or Ansible and infrastructure level applications, such as Kubernetes, Terraform, MySQL, etc.
- In depth understating of container technologies like Docker, Enroot, etc.
- Experience programming in Python and Bash scripting
Ways to stand out from the crowd:
- Familiarity with NVIDIA GPUs, Cuda Programming, NCCL and MLPerf benchmarking
- Familiarity with InfiniBand with IBoIP 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
NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most brilliant and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you.
The base salary range is 148,000 USD - 276,000 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.
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.