Senior Software Engineer – Storage

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Design, develop, and manage distributed systems that oversee data, computation, and networking for extensive AI workloads.

• Create software and automation tools to orchestrate tasks across thousands of GPUs and petabytes of storage within multi-region clusters.

• Work in collaboration with AI/ML research teams to comprehend requirements and convert them into scalable, high-performance solutions.

• Lead efforts to enhance system reliability, performance, and observability to achieve exascale standards.

• Collaborate with security, networking, and platform teams to ensure that the MARS infrastructure complies with robustness and compliance standards.

• Engage in design reviews, contribute to discussions on system architecture, and influence NVIDIA’s AI infrastructure stack.

• Remain updated on advancements in distributed systems, large-scale computing, and AI frameworks to influence the future direction of MARS.

⛳️ Requirements

• Bachelor’s degree or equivalent experience in Computer Science, Computer Engineering, or a related technical discipline.

• Over 5 years of experience in developing and managing large-scale distributed systems, infrastructure platforms, or HPC environments.

• Proficient programming skills in C++, Python, or Go, with a proven background in designing production-quality software systems.

• Strong grasp of distributed systems principles, data management, and large-scale orchestration frameworks.

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

• Familiarity with cloud platforms (Azure, AWS, GCP) and infrastructure automation technologies.

• Exceptional problem-solving abilities, a sense of ownership, and the capability to excel in a dynamic, collaborative environment.

• Outstanding communication skills and a history of cross-functional teamwork.

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

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

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

• Understanding of data security, compliance, and lifecycle management for research-scale datasets.

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

🏝️ Benefits

• Equity

• Benefits