Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leader in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The Senior Cloud Software Engineer will build cloud-native systems and services for managing data across hybrid and multi-cloud infrastructure to support high-performance AI workflows.
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
Skills
- 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
Benefits
- Equity
- Benefits
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