Principal AI and ML Infra Software Engineer, GPU Clusters

NVIDIA•Redmond, WA

4d•$272,000 - $431,250

About The Position

NVIDIA is seeking a Principal AI and ML Infra Software Engineer to join its Hardware Infrastructure team and improve efficiency for AI and ML researchers across the infrastructure stack. The role partners closely with researchers to identify infrastructure deficiencies and build effective, scalable solutions that support AI and ML research on GPU clusters. The engineer will lead initiatives to remove efficiency bottlenecks, shape long-term roadmaps, and help define measurable indicators of researcher efficiency. The position also focuses on monitoring and optimizing infrastructure performance, availability, scalability, and resource utilization. The engineer will collaborate with researchers, data engineers, and DevOps professionals to develop a cohesive AI/ML infrastructure ecosystem, while staying current with advances in AI/ML technologies, frameworks, and practices and advocating for their adoption.

Requirements

Bachelor of Science or similar background in Computer Science or a related area, or equivalent experience.
15+ years of demonstrated expertise in AI/ML and HPC tasks and systems.
Hands-on experience using or operating HPC-grade infrastructure and in-depth knowledge of accelerated computing, storage, scheduling and orchestration, high-speed networking, and container technologies.
Experience supervising and improving substantial distributed training operations using PyTorch (DDP, FSDP), NeMo, or JAX.
In-depth understanding of AI/ML workflows, including data processing, model training, and inference pipelines.
Proficiency in programming and scripting languages such as Python, Go, and Bash.
Familiarity with cloud computing platforms such as AWS, GCP, or Azure, and experience with parallel computing frameworks and paradigms.
Dedication to ongoing learning and staying current with new technologies and methods in AI/ML infrastructure.
Excellent communication and collaboration skills, including the ability to work effectively with people and teams from different backgrounds.

Responsibilities

Work with AI and ML research teams to understand infrastructure requirements and barriers and turn those insights into improvements.
Identify researcher-efficiency bottlenecks and lead initiatives and long-term roadmaps to address them.
Monitor and optimize infrastructure performance, availability, scalability, and resource utilization.
Help define and improve measures of AI researcher efficiency and align initiatives with measurable results.
Collaborate with researchers, data engineers, and DevOps professionals to develop a cohesive AI/ML infrastructure ecosystem.
Keep up to date with developments in AI/ML technologies, frameworks, and practices, and advocate for their integration.