Senior Storage Software Engineer, DGXC Data Services

🕒 July 6

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Logo of NVIDIA

NVIDIA

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure. • Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration. • Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks. • Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads. • Work closely with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments. • Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, performance, and verification.

🎯 Requirements

• BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience. • Strong foundation in algorithms, data structures, distributed systems, operating systems, and practical software design. • Experience building performance-sensitive systems, storage, backend, or cloud-native software in languages such as Go, Python, Rust, C/C++, or Java. • Experience with storage systems, object stores, caching, Linux systems, Kubernetes, or cloud infrastructure. • Ability to reason about performance, scalability, concurrency, reliability, and operational tradeoffs in production systems. • Ability to design APIs, document systems, communicate clearly, and break ambiguous infrastructure problems into practical execution plans. • Curiosity and practical judgment around AI-assisted or agentic engineering workflows, including using clear intent, specifications, acceptance criteria, tests, and verification to guide development.

🏖️ Benefits

• equity • benefits

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