About the job
We are seeking a Senior 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!
Responsibilities
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.
Qualifications
Minimum
BS or similar background in Computer Science or related area (or equivalent experience)
Minimum 5+ years of 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
Preferred
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