What skills does NVIDIA seek for the Senior Cloud Services Software Engineer role?

NVIDIA values robust backend development experience, proficiency in Python, Go, or C/C++, and a strong background in distributed systems, cloud platforms, and HPC.

What interview topics should I prepare for at NVIDIA?

Focus on distributed systems design, microservices architecture, API integration, and resilience strategies in cloud and HPC environments.

How can my past projects support my application for this role at NVIDIA?

Detail projects that showcase backend development, working with large-scale AI or HPC systems, and contributions to scalable infrastructure solutions.

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This job post expired on November 11, 2025

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Senior Cloud Services Software Engineer

NVIDIA

HybridHybrid

Original Job Summary

Overview

Join NVIDIA's DGX Cloud Team and contribute to the infrastructure powering innovative AI research. As a Senior Cloud Services Software Engineer, you will develop and optimize AI infrastructure services to deliver peak performance and resiliency for DGX Cloud.

Responsibilities

Develop solutions integrating machine learning, distributed systems, and HPC.
Design and optimize micro-services orchestrated by Kubernetes for large-scale AI workflows.
Co-design and implement APIs for integration with NVIDIA's resiliency stacks.
Create abstractions for long-running training jobs with auto-restart capabilities.
Develop modular services deployable on on-premises AI clusters.

Requirements

A Bachelor's degree in Computer Science or related field and at least 12 years of hands-on experience in backend development with languages such as Python, Go, or C/C++. Proven record in building large-scale distributed systems, experience with cloud platforms (AWS, Azure, GCP), container technologies like Docker and Kubernetes, and HPC/AI platforms such as Slurm.

Preferred Qualifications

Experience with DL frameworks and orchestrators (PyTorch, TensorFlow, JAX, Ray).
Background in framework plugin architectures and cluster scheduler integration.
Deep understanding of NVIDIA GPUs, network technologies, and failure patterns.
Practical experience with AI models and AI-based tools, plus code contributions.

About NVIDIA

NVIDIA leads groundbreaking developments in AI, HPC, and visualization. Work with world-class engineers to shape the future of technology.

Key Skills/Competency

Distributed Systems
Backend Development
Cloud Computing
Kubernetes
Python
Go
Microservices
High-Performance Computing
API Development
AI Infrastructure

How to Get Hired at NVIDIA

🎯 Tips for Getting Hired

Research NVIDIA's culture: Review mission, values, and recent projects.
Customize your resume: Highlight backend, cloud, and AI experience.
Tailor your portfolio: Showcase distributed systems and microservices.
Prepare for interviews: Familiarize with Kubernetes and HPC concepts.

📝 Interview Preparation Advice

Technical Preparation

Review Kubernetes deployment and configurations.

Practice coding in Python and Go.

Study distributed system design and resilience.

Explore cloud platform services and API integration.

Behavioral Questions

Describe past team collaboration experiences.

Explain problem-solving in distributed systems.

Detail experiences handling project failures.

Discuss managing high-pressure technical challenges.

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