Principal Software Engineer — Agentic AI Applications and Foundations
at Nvidia
USD 272,000-431,200 per year
Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Go @ 4
TypeScript @ 4
Python @ 4
Java @ 4
CI/CD @ 7
Hiring @ 4
Leadership @ 7
Communication @ 7
JavaScript @ 4
React @ 4
Debugging @ 7
API @ 4
Design Patterns @ 4
LLM @ 4
GPU @ 4
Observability @ 4
AI @ 4
Agentic AI @ 4
RAG @ 4
TensorRT @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA's Enterprise AI team builds intelligent AI agents used across the company — from smart personal assistants and engineering-productivity tools to data-driven analytics and supply-chain optimization. This principal-level, hands-on engineering leader role focuses on hardening production Agentic AI applications and architecting the next generation of agent infrastructure. This is an engineering/production role (not research) emphasizing reliability, polish, user trust, and scalable architecture.
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.
- 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.
- 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 durable, reusable foundations.
- Define 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.
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. Technologies mentioned include 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 (production-grade systems around AI; not a research scientist requirement).
- Strong judgment, communication, and cross-functional leadership skills, with the ability to influence across teams while remaining highly hands-on.
Ways to Stand Out
- Experience hardening desktop or client applications at scale, including installers, auto-update systems, crash recovery, and enterprise distribution.
- 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 (orchestration layers, memory/context services, evaluation platforms, human-in-the-loop workflows, 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.
Compensation & Benefits
- Base salary range: 272,000 USD - 431,250 USD.
- Eligible for equity and benefits (link to NVIDIA benefits referenced in the posting).
Other Information
- Applications accepted at least until April 10, 2026. This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and values diversity in hiring and promotion practices.