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Senior GPU Resource Optimization Specialist
2 months ago
NVIDIA has been at the forefront of innovation for over two decades. Our creation of the GPU in 1999 not only propelled the PC gaming industry but also transformed modern graphics and parallel computing.
Recently, the advent of GPU deep learning has ushered in a new era of artificial intelligence — a pivotal moment in computing history.At NVIDIA, we pride ourselves on being a 'learning machine' that continuously adapts to tackle complex challenges that are unique to us and impactful to the world.
This is our mission: to enhance human creativity and intelligence. We invite you to consider joining our team.
As a key member of the GPU AI/HPC Infrastructure team, you will play a crucial role in the design and execution of cutting-edge GPU compute clusters that support demanding deep learning, high-performance computing, and resource-intensive tasks.
Your contributions will be vital in addressing strategic challenges related to maximizing and optimizing our utilization of all data center resources, including compute, storage, networking, and power.
You will develop methodologies, tools, and metrics to facilitate effective resource usage in a diverse computing environment and assist in planning for growth across our global computing landscape.
Key Responsibilities:
- Enhancing our ecosystem surrounding GPU-accelerated computing, including the development of large-scale automation solutions.
- Assisting researchers in executing their workflows on our clusters, including performance assessments and optimizations of deep learning processes.
- Identifying customer utilization gaps and job scheduling challenges.
- Creating automation, tools, and metrics to boost productive resource utilization.
- Collaborating with the scheduling team to refine scheduling algorithms.
- Conducting root cause analyses and recommending corrective measures for issues of varying scales.
- Proactively identifying and resolving potential problems.
Qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
- A minimum of 5+ years of experience in designing and managing large-scale compute infrastructure.
- Proven experience in analyzing and optimizing performance for various AI/HPC workloads.
- Familiarity with cluster configuration management tools such as Ansible, Puppet, or Salt.
- Experience with advanced job schedulers in AI/HPC, ideally with knowledge of schedulers like Slurm, K8s, RTDA, or LSF.
- Understanding of container technologies such as Docker, Singularity, Shifter, or Charliecloud.
- Proficiency in Python programming and bash scripting.
- Experience with AI/HPC workflows utilizing MPI.
Preferred Qualifications:
- Experience with NVIDIA GPUs, CUDA programming, NCCL, and MLPerf benchmarking.
- Knowledge of Machine Learning and Deep Learning concepts, algorithms, and models.
- Proficiency in CentOS/RHEL and/or Ubuntu Linux distributions.
- Familiarity with InfiniBand, IBOP, RDMA, and an understanding of high-speed distributed storage systems like Lustre and GPFS for AI/HPC workloads.
- Experience with deep learning frameworks such as PyTorch and TensorFlow.
NVIDIA offers competitive salaries and a comprehensive benefits package. Our team consists of some of the brightest minds in the industry, and due to our rapid growth, our world-class engineering teams are expanding quickly.
If you are a creative and independent engineer with a genuine passion for technology, we would love to hear from you.NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We value diversity in our current and future employees and do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.
