Principal Software Engineer — Agentic AI Applications and Foundations

NVIDIA•Santa Clara, CA

About The Position

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-accelerated 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, we are increasingly known as “the AI computing company.” Our Enterprise AI team builds intelligent AI agents that transform how NVIDIA operates — from smart personal assistants and engineering-productivity tools to data-driven analytics and supply-chain optimization. These agents are live, in production, and used across the company. Now we need a principal-level, hands-on engineering leader to make them bulletproof and to architect the next generation of agent infrastructure. This is not a research role. This is a role for someone who obsesses over reliability, polish, and user trust — and who has the full-stack depth to harden production systems and the architectural vision to ensure they scale.

Requirements

BS, MS, or equivalent experience in Computer Science or a related field.
15+ years building and operating production software systems, including significant experience leading architecture and delivery across the full stack.
Solid experience building modern applications across frontend, backend, and platform layers. This may include technologies such as TypeScript/JavaScript, React, Electron or similar desktop frameworks, Python, Go, Java, APIs, data systems, and distributed infrastructure.
Proven track record taking complex products from prototype to reliable, secure, well-operated production systems.
Deep expertise in testing strategy, release engineering, observability, performance tuning, and incident response.
Experience building shared services, internal platforms, SDKs, or core infrastructure used by multiple teams or products.
Working knowledge of modern AI application patterns such as LLM-powered applications, RAG, tool use, CLI-based workflows, reusable skills, MCP-based integrations, evaluation loops, memory systems, and agentic workflows. You do not need to be a research scientist, but you should know how to build reliable, production-grade systems around AI.
Strong judgment, communication, and cross-functional leadership skills, with the ability to influence across teams while remaining highly hands-on.

Nice To Haves

Experience hardening desktop or client applications at scale, including installers, auto-update systems, crash recovery, and enterprise distribution.
A track record of improving engineering velocity and consistency through common frameworks, platform services, design patterns, and developer tooling.
Experience building reusable infrastructure for AI products, such as orchestration layers, memory/context services, evaluation platforms, human-in-the-loop workflows, or policy and safety controls.
Familiarity with identity, discovery, trust, reputation, or graph-based systems relevant to large-scale agent collaboration.
Experience with GPU-accelerated systems or NVIDIA AI technologies such as NeMo, NIM, Nemotron, TensorRT-LLM, or AI Blueprints.

Responsibilities

Improve reliability, performance, observability, release confidence, and end-user experience across desktop, web, and service-based AI products.
Design and build resilient frontends, backend APIs, distributed services, data flows, and deployment systems that scale to enterprise use.
Establish strong patterns for testing, debugging, CI/CD, safe rollout, auto-update mechanisms, monitoring, incident response, and operational excellence so our Agentic AI applications behave like mature software, not prototypes.
Build reusable capabilities that support multiple agent domains, including orchestration services, deep-agent workflows, memory and context services, evaluation frameworks, telemetry, and policy-aware tool integration.
Help validate and operationalize technologies such as Nemotron, NVIDIA AI Blueprints, and related platform capabilities in enterprise production settings.
Codify architecture, shared components, documentation, and operational playbooks; mentor engineers; and create foundations that are durable, reusable, and broadly owned.
Define the core architecture for how AI agents discover one another, collaborate securely, build trust, and operate under enterprise governance.
Partner closely with domain AI engineers, product managers, designers, infrastructure teams, IT, and research to deliver measurable outcomes across employee productivity, engineering efficiency, AIOps, and enterprise operations.