Note: The job is a remote job and is open to candidates in USA. NVIDIA’s DGXC Data Services team builds cloud-native data and storage infrastructure for hybrid and multi-cloud environments. The Senior Cloud Software Engineer develops scalable services and APIs for AI data workflows and collaborates across engineering, product, platform, and operations teams to deliver reliable 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

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

• You will also be eligible for equity

Company Overview

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