Nvidia Senior AI Performance Efficiency Engineer 

China, Shanghai 

624187883

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)

• Minimum 8+ 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

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