Nvidia Principal Infrastructure Performance Development Engineer 

United States, California 

610742799

as developing

What you will be doing:

• Build tools and frameworks that provide real time application performance metrics that can be correlated with system metrics

• Develop automation frameworks that empower applications to thoughtfully predict and overcomesystem/infrastructurefailures, ensuring fault tolerance.

• Collaborate with software teams to pinpoint performance bottlenecks. Design, prototype, and integrate solutions that deliver demonstrable performance gains in production environments.

• Adapt and enhance communication libraries to seamlessly support innovative network topologies and system architectures.

• Design or adapt optimized storage solutions to boost Deep Learning efficiency, resilience, and developer productivity.

What We Need to See:

• BS/MS/PhD (or equivalent experience) in Computer Science, Electrical Engineering or a related field.

• Proven experience in least one of the following area:

10+ years of experience in analyzing and improving performance of training applications using PyTorch or similar framework

10+years of experience with building distributed software applications

10+years of experience in building storage solutions for Deep Learning applications

10+ years of background in building automated fault tolerant distributed applications

5+ years building tools for bottleneck analysis and automation of fault tolerance in distributed environments.

• Strong background in parallel programming and distributed systems

• Experience analyzing and optimizing large scale distributed applications.

• Excellent verbal and written communication skills

Ways To Stand Out From The Crowd:

• Deep understanding of HPC and distributed system architecture with emphasis on RDMA

• Hands on working experience in more than one of the above areas especially with performance analysis and profiling of Deep Learning workloads.

• Comfortable navigating and working with the PyTorch codebase.

• Proven understanding of CUDA and GPU architecture