Senior Software Engineer, Infrastructure and Tooling - DriveOS

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA is seeking a Senior Software Engineer for its DriveOS Infrastructure and Tooling team. DriveOS is a software platform for autonomous vehicles and other intelligent machines, combining system software, virtualization, and accelerated computing for safety-critical applications. This role focuses on building coherent, practical, and scalable engineering infrastructure and tooling that help teams deliver reliable software and evidence appropriate to product risk. The engineer will combine software development and AI-enabled automation to translate quality, safety, and cybersecurity expectations into integrated engineering workflows. The role includes embedding intelligent agents, automated reasoning, and data-driven insights into development processes to improve efficiency and product quality. The position also involves partnering across a globally distributed organization, shaping infrastructure roadmaps, consolidating tooling initiatives, and coaching engineers. The role develops highly available automation systems, developer tooling, dashboards, AI-native pipelines, and data pipelines. It also includes operationalizing emerging AI capabilities and establishing governance for AI-assisted outputs, with traceability, auditability, and compliance when generated artifacts are used in safety-certified processes. The posting lists base salary ranges of $184,000–$287,500 for Level 4 and $224,000–$356,500 for Level 5. The employee is also eligible for equity and benefits.

Requirements

BS or MS in Computer Science, or equivalent experience.
8+ years of experience 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 such as GitLab CI, Jenkins, or similar, and infrastructure-as-code tools such as Terraform and Ansible.
Experience building production-grade full-stack systems, including backend microservices using Python, Node.js, or Go; RESTful and GraphQL APIs; relational and NoSQL datastores; graph databases such as Neo4j; and modern web interfaces using 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.

Nice To Haves

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, such as VS Code extensions, IDE plugins, or 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.

Responsibilities

Design and implement highly available automation systems, developer tooling, and intelligent dashboards to 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 incorporating LLM-based agents, RAG systems, and multi-agent orchestration into engineering workflows, including requirements analysis, test generation, defect triage, and safety documentation.
Translate requirements from cross-functional collaborators into process-automation solutions aligned with automotive standards ISO 26262 and ISO 21434 and quality model ISO 25010.
Build integrated data pipelines that aggregate telemetry from global systems and present 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 to ensure traceability, auditability, and compliance when AI-generated artifacts feed into safety-certified processes.
Raise standards for tooling quality and architectural guidelines.
Coach and support engineers at all levels and foster a culture of technical growth.