Senior Cloud Software Engineer, DGXC Data Services

NVIDIA is a technology company advancing artificial intelligence, high-performance computing, and visualization. The Senior Cloud Software Engineer will build and operate cloud-native data and storage services, APIs, and infrastructure for hybrid and multi-cloud environments supporting large-scale GPU training and inference. The role also involves collaborating across product, engineering, SRE, operations, and support teams to deliver reliable, secure, observable production systems.

Responsibilities:

• Build cloud-native data and storage services for hybrid and multi-cloud infrastructure, including dataset discovery, ingestion, governance, checkpointing, observability, and low-latency access
• Develop scalable cloud-native services and APIs that support exabyte-scale, high-performance GPU training and inference workflows
• Work closely with product managers, internal AI teams, platform teams, and partner engineering teams to understand requirements and turn them into reliable production systems
• Collaborate with SRE, operations, and support teams to improve service reliability, performance, observability, on-call readiness, and operational scale
• Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, 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, and practical software design
• Experience building, shipping, and operating backend or cloud-native services using Kubernetes, cloud providers such as AWS, GCP, or Azure, and languages such as Go, Python, Rust, C/C++, or Java
• Ability to design APIs, document systems, reason through tradeoffs, communicate clearly, and break ambiguous problems into practical execution plans
• Experience working across engineering, product, platform, and operations teams to deliver reliable production software
• Curiosity and practical judgment around AI-assisted or agentic engineering workflows, including using clear intent, specifications, acceptance criteria, tests, and verification to guide development
• Hands-on experience building, scaling, or operating large-scale data, storage, or ML infrastructure services
• Experience solving enterprise-grade data management, governance, analytics, or AI workflow problems with modern data and ML infrastructure technologies
• Strong background in distributed systems, storage systems, cloud infrastructure, performance engineering, observability, or agentic engineering practices