Senior Applied AI Engineer

at Nvidia

USD 152,000-287,500 per year

SENIOR

✅ On-site

Tech Stack
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.

AI @ 6 API @ 7 CI/CD @ 6 Claude Code @ 4 Codex @ 4 Communication @ 6 GitHub @ 4 LLM @ 6 Mentoring Observability @ 6 Python @ 7 Security @ 6 Software Development @ 4

Details

NVIDIA is hiring for an Applied AI team for chip design. You will collaborate with researchers to design and scale agents, and build/maintain core infrastructure for deploying and running these agents in production.

Responsibilities

Design, develop, and improve scalable infrastructure to support the next generation of AI applications, including copilots and agentic tools.
Drive improvements in architecture, performance, and reliability, enabling teams to bring to bear LLMs and advanced agent frameworks at scale.
Collaborate across hardware, software, and research teams, mentoring and supporting peers while encouraging best engineering practices and a culture of technical excellence.
Stay informed of the latest advancements in AI infrastructure and contribute to continuous innovation across the organization.

Requirements

MS or higher degree (or equivalent experience) in Computer Science, Engineering, AI, or a related technical field, with 5+ years of hands-on software engineering experience building production-grade software systems, and demonstrated experience shipping AI/LLM-powered applications, agents, or automation workflows into real production environments.
Strong Python engineering skills are preferred, with the ability to design, prototype, and productionize AI-enabled services, APIs, integrations, automation workflows, and internal tools.
Practical experience building LLM-powered agents or agentic workflows, with hands-on use of Claude Code, OpenAI Codex, Cursor, GitHub Copilot, or equivalent coding agents to improve real software development workflows.
Solid software engineering fundamentals and production mindset, including system design, API design, testing, CI/CD, code quality, observability, security, databases, containers, and distributed or event-driven systems.
Ability to identify repetitive, high-friction, or knowledge-intensive workflows and turn them into practical AI-enabled tools, automations, or assistants that improve productivity and operational efficiency.
Demonstrated end-to-end ownership of engineering solutions, from architecture and development to deployment, integration, and ongoing operations/support.
Excellent communication skills and a collaborative, proactive approach.