The NVIDIA Networking Advanced Development Software group builds groundbreaking technologies that open new markets and deepen customer relationships. We focus on emerging areas at the intersection of networking and AI — including AI-driven development tools, high-performance networking for AI factories and data centers, and intelligent automation across the software lifecycle. Our work spans the full stack: from application-level analysis and architecture definition down to implementation, leveraging NVIDIA's world-leading networking devices. We collaborate with partners and key customers throughout the process and actively engage with open-source communities.
Within this group, our team is building an AI engineering platform — a cross-domain, distributed and multi-disciplinary systems that enable AI agentic workflows to accelerate engineering development processes, on all stages of the development cycle. The platform combines multiple technologies within the industry standards, introducing new ways to handle complex tasks. Our technological stack based on K8s, AI harness agent, RAG, MCPs, and top tier LLMs. It is designed to drive the next generation of fully autonomous, long living agentic workflows, in scale and best performance in the industry.
What you'll be doing:
Design and build the core platform that powers an autonomous AI agent — including its reasoning engine, tool orchestration, and the runtime infrastructure it operates on
Develop and evolve the Micro-services ecosystem that gives the agent its capabilities — from knowledge retrieval and log analysis to code execution and workflow automation
Own features end-to-end: from requirements analysis and architecture, through implementation, to production deployment and iteration based on real usage
Instrument, evaluate, and improve the platform's reliability — build observability, track quality, and feed signals back into the system to make the agent more effective over time
Collaborate with engineering teams across the organization to identify high-impact workflows and translate them into AI-assisted automation that boosts developer productivity
Work across the stack when the problem requires it — Python services, Kubernetes infrastructure, data stores, CI/CD pipelines, and developer-facing tools
What we need to see:
B.Sc. in Computer Science, Computer Engineering, or a related field
Solid system-level understanding with experience designing and delivering production services
Ability to architect solutions, guide AI tools effectively, and reason about system behavior end-to-end
Familiarity with containerization and orchestration (Docker, Kubernetes)
Understanding of REST APIs, microservice architectures, and distributed systems
Ability to learn complex concepts in a fast-paced environment
A teammate with a can-do attitude, high energy, and excellent interpersonal skills
Ways to stand out from the crowd:
Familiarity with Kubernetes operators, Helm charts, and cluster management
Experience with LLM application development — prompt engineering, agentic frameworks (ReAct, tool-use), or RAG pipelines
Hands-on experience with FastAPI, async Python, or similar modern Python web frameworks
Experience with vector databases, semantic search, or embedding models
Knowledge of OAS (OpenAPI Specification), MCP (Model Context Protocol), and A2A (Agent-to-Agent) protocol ecosystem
NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Skills Required
- Bachelor of Science degree in Computer Science, Computer Engineering, or a related field
- System-level understanding and experience designing and delivering production services
- Ability to architect solutions, guide AI tools, and reason about system behavior end to end
- Familiarity with Docker and Kubernetes containerization and orchestration
- Understanding of REST APIs, microservice architectures, and distributed systems
- Ability to learn complex concepts in a fast-paced environment
- Excellent interpersonal skills and collaborative, can-do attitude
- Familiarity with Kubernetes operators, Helm charts, and cluster management
- Experience with LLM application development, prompt engineering, agentic frameworks, or RAG pipelines
- Hands-on experience with FastAPI, async Python, or similar modern Python web frameworks
- Experience with vector databases, semantic search, or embedding models
- Knowledge of OpenAPI Specification, Model Context Protocol, and Agent-to-Agent protocols
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
