Senior System Software Engineer - Local AI

About the role

NVIDIA has continuously reinvented itself for over two decades. The invention of the GPU in 1999 fueled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning has ignited modern AI, positioning NVIDIA as a leading AI computing company.

Artificial intelligence is shifting from passive assistance to autonomous, always-on workflows. Our mission is to make this transition seamless, efficient, and secure for millions globally. The Local AI Inference team is seeking a Senior System Software Engineer to lead the development of robust system-level software and infrastructure for deploying, benchmarking, and qualifying high-quality AI applications and models across Linux and Windows systems powered by RTX and DGX-class GPUs. You will partner with inference engineers, model developers, and AI application teams to build reliable, scalable infrastructure that enables reproducible evaluation and improves local AI performance. Using AI tools and developing autonomous agents, you will accelerate infrastructure development, automate operations, and help engineers diagnose complex system issues. If you enjoy tackling systems challenges and shaping how AI infrastructure is built and operated, we would love to hear from you!

What you'll be doing:

- Partner across organizations with benchmarking teams, inference teams, and AI application developers to define requirements, design, and maintain shared infrastructure for deploying, benchmarking, and qualifying AI workloads across infernece backends such as llama.cpp, Ollama, PyTorch, vLLM, and WinML.

- Build Git-based CI infrastructure for automated builds, integration testing, and regression detection across hardware platforms, operating systems, and runtime configurations.

- Develop model management services to download, version, cache, and synchronize checkpoints and dependencies for reproducible execution.

- Design databases, backend services, and data pipelines to store and query model metadata, benchmark results, system telemetry, and execution artifacts.

- Create dashboards and reports that help engineers compare inference configurations, identify regressions, and investigate performance bottlenecks.

- Develop and operate server infrastructure, including environment provisioning, workload scheduling, resource management, and failure recovery.

- Develop autonomous AI agents for infrastructure management, workload orchestration, monitoring, and diagnostics, with appropriate access controls and execution safeguards.

- Maintain Git repositories and companion websites, keeping code, documentation, model recommendations, and evaluation results current.

- Collaborate with inference developers to implement instrumentation and debugging capabilities, resolve system issues, and improve infrastructure efficiency and scalability.

What we need to see:

- B.Tech or higher degree in Computer Science, Computer Engineering, Information Technology, Software Engineering, or a related field, or equivalent experience.