Senior and Development Engineer

Overview

Joining NVIDIA's AI Efficiency Team means contributing to the infrastructure that powers our leading-edge AI research. This team focuses on optimizing efficiency and resiliency of ML workloads, as well as developing scalable AI infrastructure tools and services. Our objective is to deliver a stable, scalable environment for NVIDIA's AI researchers, providing them with the necessary resources and scale to foster innovation.

We’re redefining the way Deep Learning applications run on tens of thousands of GPUs. Join our team of experts and help us build a supercharged AI platform that improves efficiency, resilience, and Model FLOPs Utilization (MFU). In this position you will be collaborating with a diverse team that cuts across many areas of Deep Learning HW/SW stack in building a highly scalable, fault tolerant and optimized AI platform.

Pay and other compensation details are described in the job post.

Base pay range: $/yr - $/yr

• Build AI models, 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 overcome system/infrastructure failures, 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.

• BS/MS/PhD (or equivalent experience) in Computer Science, Electrical Engineering or a related field.
• 12+ years of proven experience in the following area:
• Analyzing and improving performance of training applications using PyTorch or similar framework
• Building distributed software applications using collective communication libraries such as MPI or NCCL or UCC
• Construct storage solutions for Deep Learning applications
• Building automated fault tolerant distributed applications
• 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

• Deep understanding of HPC and distributed system architecture.
• Hands on working experience in more than one of the above areas especially with large SOTA AI models, performance analysis and profiling of Deep Learning workloads.
• Comfortable navigating and working with the PyTorch codebase.
• Proven understanding of CUDA and GPU architecture

• Mid-Senior level

• Full-time

• Industries

Industries:
Computer Hardware Manufacturing, Software Development, and Computers and Electronics Manufacturing