Nvidia Senior AI ML Storage Engineer 

United States, Texas 

307877681

What You’ll Be Doing:

• Design, develop, and operate distributed systems that manage data, compute, and networking for large-scale AI workloads.

• Build software and automation to orchestrate workloads across thousands of GPUs and petabytes of storage in multi-region clusters.

• Collaborate with AI/ML research teams to understand their requirements and translate them into scalable, high-performance solutions.

• Drive improvements in system reliability, performance, and observability to meet exascale standards.

• Partner with security, networking, and platform teams to ensure that MARS infrastructure meets the highest standards of robustness and compliance.

• Participate in design reviews, contribute to system architecture discussions, and influence the evolution of NVIDIA’s AI infrastructure stack.

• Stay current with advances in distributed systems, large-scale computing, and AI frameworks to help shape the future direction of MARS.

What We Need to See:

• BS or equivalent experience in Computer Science, Computer Engineering, or a related technical field.

• 8+ years of experience developing and operating large-scale distributed systems, infrastructure platforms, or HPC environments.

• Strong programming skills in C++, Python, or Go, with proven experience designing production-quality software systems.

• Solid understanding of distributed systems principles, data management, and large-scale orchestration frameworks.

• Hands-on experience with high-performance storage (e.g., Lustre, GPFS, BeeGFS) and compute scheduling and orchestration (e.g., Slurm, Kubernetes, LSF).

• Familiarity with cloud environments (Azure, AWS, GCP) and infrastructure automation tools.

• Strong problem-solving skills, ownership mindset, and the ability to thrive in a fast-paced, collaborative environment.

• Excellent communication skills and a track record of cross-functional collaboration.

Ways to Stand Out from the Crowd:

• Graduate degree (MS/PhD or equivalent experience) in Computer Science, Distributed Systems, or a related field.

• Expertise in large-scale data management, cluster scheduling, or workload orchestration at exascale scale.

• Experience building or maintaining infrastructure for AI/ML research, including distributed training pipelines using PyTorch, JAX, or NeMo.

• Familiarity with data security, compliance, and lifecycle management for research-scale datasets.

• Demonstrated leadership in system architecture design, performance optimization, or reliability engineering.