Principal Engineer, AI Tooling and Workflows

NVIDIA•Us, CA

About The Position

NVIDIA's Infrastructure, Planning and Process (IPP) Team is seeking a Principal Software Engineer to lead the next generation of AI-powered engineering platforms. In this role, you will define and build agentic AI systems, developer productivity platforms, and intelligent workflow automation that accelerate software delivery across NVIDIA's engineering organization. Your work will help thousands of engineers move faster and improve quality and will also reduce manual overhead for various workflows. This is a high-impact role for a technology lead who can operate across strategy, architecture, execution, and influence!

Requirements

PhD or MS or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
15+ years of software engineering experience.
Experience in large-scale platforms, distributed systems, AI systems, or developer infrastructure used by demanding engineering teams.
Deep hands-on expertise with LLM applications, agentic workflows, RAG, embeddings, vector search, tool use, prompt engineering, model evaluation, and AI system safety.
Exceptional architecture judgment across APIs, services, data pipelines, Kubernetes, observability, reliability engineering, security, and production operations.
Strong coding ability in Python and at least one major production language such as C++, Go or Rust, with the judgment to build simple systems that scale.
Technical leadership at Principal level: setting direction, aligning collaborators, guiding senior engineers, and raising the engineering bar across boundaries.

Nice To Haves

Built AI tools, copilots, or autonomous agents that materially changed how large engineering organizations build, validate, or operate software.
Understanding of the full stack of enterprise AI systems: MCPs, tool-using agents, skills, retrieval, knowledge graphs, fine-tuning, model serving, evaluation, governance.
Optimizations in AI platforms for real-world scale, including latency, throughput, cost, GPU acceleration, TensorRT, Triton, quantization, batching, caching, or model routing.
Domain depth in GPU computing, drivers, compilers, embedded systems, robotics, autonomous vehicles, or other hardware-software environments.
Spotting step-function productivity opportunities and turning them into efficient platforms that engineers love and leaders trust.

Responsibilities

Lead the technical vision, architecture, and execution for AI-native developer tooling and workflow automation platforms used across NVIDIA engineering.
Invent and develop production-grade autonomous AI systems that can reason over engineering workflows - code, documentation, CI/CD pipelines.
Drive the evolution of AI-assisted processes in software development, including code understanding, requirements traceability, validation, tests, build and release automation, security review.
Define platform-level standards for reliability, evaluation, observability, safety, security, latency, cost efficiency, and human-in-the-loop controls for LLM-powered systems.
Partner with engineering leaders, teams across products, infrastructure, security, and research to identify high-leverage opportunities and deliver solutions with broad impact.
Influence technical direction across multiple teams by setting architecture patterns, reviewing designs, raising engineering standards, and mentoring senior engineers.