We are hiring a Senior AI Engineer to build and operate production AI systems and infrastructure. In this role, you will work on AI agents, MCP servers, agent evaluation, and Temporal-based workflow frameworks. This is a hands-on engineering role for someone who can move between backend systems, AI application development, deployment, and production support. You will help build reliable services and frameworks that make AI agents usable, measurable, and dependable in production.
What you’ll be doing:
- Build and deploy AI agents that use tools, call services, manage context, and execute multi-step tasks.
- Develop and maintain MCP servers and tool integrations used by internal AI systems.
- Build workflow infrastructure using Temporal or similar orchestration frameworks.
- Design and implement agent evaluation flows, test suites, quality metrics, and feedback loops. Design backend services, APIs, persistence layers, and platform components for AI applications.
- Work with SQL and NoSQL databases, queues, distributed systems, and cloud-native infrastructure.
- Build CI/CD pipelines, Docker images, Kubernetes deployments, monitoring, and operational tooling.
- Debug production issues across services, workflows, agents, tools, models, and infrastructure.
- Collaborate with engineering, product, infrastructure, and data science teams to turn requirements into reliable systems.
What we need to see:
- 5+ years of experience and a practical understanding of how AI agents are built, including tool use, state, context, and multi-step execution.
- Bachelors degree or equivalent experience.
- Experience with at least one open-source agent or LLM application framework, such as LangChain/LangGraph.
- Strong software engineering background, especially in backend development.
- Experience with distributed systems, workflow orchestration, and production service design.
- Hands-on experience with SQL and NoSQL databases.
- Experience with Docker, Kubernetes, CI/CD pipelines, and production deployment workflows.
Ways to stand out from the crowd:
- Experience working with both traditional machine learning systems and contemporary LLM or agentic applications.
- Excellent judgment about when agent-based approaches are appropriate—and when a simpler solution is more effective.
- Experience making AI behavior measurable, observable, explainable, and safe in production.
- Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost.
- A history of guiding engineers or heading cross-departmental technical projects.
We are looking for engineers who care deeply about technical quality. They take ownership from building through production. They are motivated by the challenge of turning advanced AI capabilities into reliable products.
NVIDIA has some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. We are committed to fostering a diverse work environment and 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
- 5+ years of professional experience
- Bachelor's degree or equivalent experience
- Practical understanding of building AI agents, including tool use, state, context, and multi-step execution
- Experience with at least one open-source agent or LLM application framework, such as LangChain or LangGraph
- Strong software engineering background, especially in backend development
- Experience with distributed systems, workflow orchestration, and production service design
- Hands-on experience with SQL and NoSQL databases
- Experience with Docker, Kubernetes, CI/CD pipelines, and production deployment workflows
- Experience with traditional machine learning systems and contemporary LLM or agentic applications
- Experience making AI behavior measurable, observable, explainable, and safe in production
- Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost
- History of guiding engineers or leading cross-departmental technical projects
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.”
