Senior Software Engineer, Infrastructure and Tooling - DriveOS

NVIDIA is harnessing the power of AI and high-performance computing to build the future of mobility. DriveOS is the software platform behind autonomous vehicles and other intelligent machines, combining system software, virtualization, and accelerated computing capabilities for safety-critical applications. Our Infrastructure and Tooling team makes the engineering environment behind that platform coherent, practical, and scalable — and we’re looking for a Senior Software Engineer to help shape it.

This role combines software engineering and AI-enabled automation with first-principles thinking: building tools that help teams deliver reliable software and evidence that is fit for product risk. You will help a globally distributed organization turn quality, safety, and cybersecurity expectations into integrated engineering workflows — embedding intelligent agents, automated reasoning, and data-driven insights where they improve development efficiency and product quality.

What you’ll be doing:

• Design and implement highly available automation systems, developer tooling, and intelligent dashboards that improve and monitor the quality of DriveOS builds and releases.
• Partner with teams across the organization to shape infrastructure roadmaps and consolidate tooling initiatives.
• Architect AI-native pipelines that integrate LLM-based agents, RAG systems, and multi-agent orchestration into core engineering workflows — automating tasks such as requirements analysis, test generation, defect triage, and safety documentation.
• Translate multi-functional collaborator requirements into process-automation solutions that operate in environments aligned with automotive standards (ISO 26262, ISO 21434) and quality models (ISO 25010).
• Build integrated data pipelines that aggregate telemetry from global systems and surface executive-level insights through advanced visualization and predictive quality metrics.
• Evaluate and operationalize emerging AI capabilities — including code-generation copilots, agentic coding assistants, and automated documentation tools.
• Help define and deploy governance policies for AI-assisted tooling outputs, ensuring traceability, auditability, and compliance when AI-generated artifacts feed into safety-certified processes.
• Raise the standard for tooling quality and architectural guidelines. Coach and support engineers at all levels, fostering a culture of technical growth.
  

• BS or MS in Computer Science, or equivalent experience, with 8+ years in infrastructure, DevOps, or software tooling.
• Strong proficiency in Python and a solid understanding of tooling architecture and design patterns.
• Experience managing enterprise-scale CI/CD environments (GitLab CI, Jenkins, or similar) and infrastructure-as-code tools (Terraform, Ansible).
• Experience building production-grade, full-stack systems — backend microservices (Python, Node.js, Go), RESTful and GraphQL APIs, relational and NoSQL datastores, graph databases (e.g., Neo4j), and modern web interfaces (React, Next.js, or TypeScript).
• Hands-on experience deploying LLM and agentic applications, including agent frameworks, tool integration, MCP-based systems, memory and orchestration, prompt engineering, guardrails, evaluation, and RAG/GraphRAG architectures.
• Experience building internal developer portals and AI-powered interfaces, including chat-based or conversational UIs for agent interaction.
• Strong SQL and data-warehousing skills for managing high-volume metrics across multiple programs.
• Clear communication and collaborative problem-solving skills.
  

• Experience developing and deploying tools across distributed organizations.
• Prior experience in automotive, aerospace, or medical industries where process infrastructure must meet strict regulatory and safety certifications.
• Experience building plugin-based or extensible tooling platforms (VS Code extensions, IDE plugins, CLI toolchains) that embed AI capabilities directly into developer workflows.
• Experience building and maintaining AI evaluation harnesses including automated regression testing for LLM outputs, prompt versioning, and guardrails for safety-critical AI-generated artifacts.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.