Principal Engineer, AI Tooling and Workflows

Details

NVIDIA 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.

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.

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.

Ways to Stand Out from the Crowd

• 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.