We are seeking an AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. As an Engineer, you will have a pivotal role in enhancing efficiency for our researchers by implementing progressions throughout the entire stack. Your main task will revolve around collaborating closely with customers to pinpoint and address infrastructure and application deficiencies, facilitating groundbreaking AI and ML research on GPU Clusters. Together, we can craft potent, effective, and scalable solutions as we mold the future of AI/ML technology!

‍

‍

What you will be doing:

‍

• Collaborate closely with our AI/ML researchers to make their ML models more efficient leading to significant productivity improvements and cost savings
• Build tools, frameworks, and apply ML techniques to detect & analyze efficiency bottlenecks and deliver productivity improvements for our researchers
• Work with researchers working on a variety of innovative ML workloads across Robotics, Autonomous vehicles, LLM’s, Videos and more
• Collaborate across the engineering organizations to deliver efficiency in our usage of hardware, software, and infrastructure
• Proactively monitor fleet wide utilization patterns, analyze existing inefficiency patterns, or discover new patterns, and deliver scalable solutions to solve them
• Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.

‍

‍

What we need to see:

‍

• BS or similar background in Computer Science or related area (or equivalent experience)
• Experience designing and operating large scale compute infrastructure
• Strong understanding of modern ML techniques and tools
• Experience investigating, and resolving, training & inference performance end to end
• Debugging and optimization experience with NSight Systems and NSight Compute
• Experience with debugging large-scale distributed training using NCCL
• Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.
• Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.
• Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds

‍

‍

Ways to stand out from the crowd:

‍

• 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

‍