At NVIDIA, we are building the next generation of AI-native software systems. As AI evolves from assistant-based workflows to autonomous systems capable of reasoning, planning, and acting, we are looking for exceptional engineers to help define and build the underlying platforms that power this transformation.
As a Senior Software Engineer, Agentic AI Systems, you will design and develop production-scale AI systems that combine large language models, retrieval, memory, orchestration, evaluation, and tools into intelligent software capable of solving complex real-world problems. This role sits at the intersection of AI, distributed systems, software engineering, and developer productivity.
What You'll Be Doing:
Design and implement intelligent systems that can reason, plan, and execute complex multi-step workflows.
Develop architectures that combine LLMs, retrieval systems, memory, tools, and feedback loops.
Build autonomous and semi-autonomous workflows that improve engineering productivity and operational efficiency.
Design scalable backend services, APIs, and distributed systems supporting AI-native applications.
Build orchestration frameworks for multi-agent and tool-based systems.
Develop evaluation frameworks that measure accuracy, reliability, latency, and task completion.
Partner with AI researchers, infrastructure teams, product teams, and software engineering organizations.
What We Need To See:
BS, MS, or PhD in Computer Science, Computer Engineering, or a related field.
5+ years of software engineering experience. Strong proficiency in Python and modern software development practices.
Experience building scalable distributed systems and cloud-native services. Experience developing AI-enabled applications using LLMs, RAG, agent frameworks, or workflow orchestration systems.
Proven experience designing, building, and operating large-scale production software systems.
Strong understanding of distributed systems, scalability, reliability, observability, and performance engineering.
Demonstrated ability to own services from architecture and implementation through production operations and long-term maintenance.
Ability to balance rapid innovation with engineering rigor, maintainability, and operational excellence.
Ways To Stand Out From The Crowd:
Experience building production AI agents or autonomous systems. Experience with reasoning frameworks, planning systems, memory architectures, and tool-use ecosystems.
Experience with vector databases, retrieval systems, knowledge graphs, or semantic search. Experience with AI evaluation, benchmarking, and observability platforms.
Built and operated business-critical platforms serving large user bases or high-volume workloads.
Experience modernizing early-stage prototypes into robust production systems.
Deep expertise in system architecture, reliability engineering, and technical leadership.Track record of reducing operational complexity while increasing scalability and maintainability.
We are looking for engineers who can build systems, not just features. The ideal candidate has experience architecting and operating large-scale, production-grade software that remains reliable, maintainable, and extensible as it grows. While AI expertise is valuable, we place equal importance on strong software engineering fundamentals, system design, operational excellence, and long-term ownership. We are looking for engineers who see AI agents not as prompts, but as software systems.
Skills Required
- BS, MS, or PhD in Computer Science, Computer Engineering, or a related field
- 5+ years of software engineering experience
- Strong proficiency in Python
- Experience building scalable distributed systems and cloud-native services
- Experience developing AI-enabled applications using LLMs, RAG, agent frameworks, or workflow orchestration systems
- Proven experience designing, building, and operating large-scale production software systems
- Strong understanding of scalability, reliability, observability, and performance engineering
- Ability to own services from architecture through production operations and maintenance
- Experience building production AI agents, reasoning frameworks, planning systems, or memory architectures
- Experience with vector databases, retrieval systems, knowledge graphs, or semantic search
- Experience with AI evaluation, benchmarking, and observability platforms
- Experience modernizing prototypes into robust production systems and operating business-critical platforms
- Technical leadership and deep expertise in system architecture and reliability engineering
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.”
