Senior DGX Cloud AI Infrastructure Software Engineer

Joining NVIDIA's DGX Cloud AI Efficiency Team means contributing to the infrastructure that powers our innovative AI research. This team focuses on developing tools for optimizing efficiency and resiliency of AI workloads - pre-training, post-training, inference. Our objective is to deliver a stable, scalable environment for AI researchers, providing them with the necessary resources and scale to foster innovation. We are seeking an AI infrastructure software engineer to join our team. You'll be instrumental in designing, building, and maintaining AI infrastructure that enable large-scale AI training and inferencing. The responsibilities include implementing software and systems engineering practices to ensure high efficiency and availability of AI systems.

As a senior DGX Cloud AI Infrastructure software engineer at NVIDIA, you will have the opportunity to work on innovative technologies that power the future of AI and data science and be part of a dynamic, diverse, and supportive team that values learning and growth. The role provides the autonomy to work on meaningful projects with the support and mentorship needed to succeed, and contributes to a culture of blameless postmortems, iterative improvement, and risk-taking. If you are seeking an exciting and rewarding career that makes a difference, we invite you to apply now!

What you’ll be doing:

• Develop infrastructure software and tools for large-scale pre-training, post-training, and inference.

• Develop and optimize tools and libraries to improve infrastructure efficiency and resiliency.

• Co-design and implement APIs for integration with NVIDIA's resiliency stacks.

• Enhance infrastructure and products underpinning NVIDIA's AI platforms.

• Define meaningful and actionable reliability metrics to track and improve system and service reliability.

• Skilled in problem-solving, root cause analysis, and optimization.

• Root cause and analyze and triage failures from the application level to the hardware level

What we need to see:

• Minimum of 8+ years of experience in developing software infrastructure for large scale AI systems.

• Bachelor's degree or higher in Computer Science or a related technical field (or equivalent experience).

• Strong debugging skills and experience in analyzing and triaging AI applications from the application level to the hardware level.

• Experience with observability platforms for monitoring and logging (e.g., ELK, Prometheus, Loki).

• Proven track record in building and scaling large-scale distributed systems.

• Experience with AI training and inferencing infrastructure services.

• Proficiency in programming languages such as Python, C/C++, script languages

• Experience in quality software engineering practices, including test development, defensive programming, version control, and CI.

• Excellent communication and collaboration skills, and a culture of diversity, intellectual curiosity, problem solving, and openness are essential.

Ways to stand out from the crowd:

• Background in working with the large scale clusters

• Experience in defining and building observability and telemetry software stack

• Experience with RDMA software stack (NCCL, IB verbs, ucx, libfabrics)

• Experience and root cause analysis of failures and datacenter scale

• Good understanding on DL frameworks internal PyTorch, TensorFlow, JAX, and Ray

NVIDIA leads the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions, from artificial in...