Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years, fueled by incredible technology and amazing people. Today, we're using the power of AI to define the next era of computing, with our GPU acting as the brains of computers, robots, and self-driving cars. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. NVIDIA is hiring a Senior Engineer to build the company's Enterprise Data Governance platform. This platform spans sensitive-information protection, governed data access across every collaboration and content system at NVIDIA, and the knowledge infrastructure that makes our enterprise AI trustworthy and accurate. What makes this role genuinely hard is the tension at its core: AI agents need broad, deep access to enterprise knowledge to be useful, while export controls, legal constraints, and security policy demand precise, auditable access control. Getting both right, across dozens of systems and one of the most complex enterprise environments in the industry, is the problem this team is solving. We are looking for technically strong engineers across the senior to principal level who thrive on hard infrastructure problems and want to own work that is central to how NVIDIA operates at scale. What You Will Be Doing: Sensitive Information Protection • Own the roadmap for sensitive-information detection and remediation, working with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust. • Evaluate and integrate ML-based classification approaches to detect sensitive content across unstructured data at scale, and iterate on precision and recall as the data landscape evolves. Governed Data Access at Scale • Drive production rollout and self-service onboarding for the enterprise data access platform, spanning connectors across email, messaging, document stores, and search (Outlook, Teams, Slack, Confluence, OneDrive, SharePoint, Google Drive, Glean). • Design and implement export-control enforcement and long-term audit logging, including authorization checks, schema design, data masking, retention, and RBAC controls across every connected system. Enterprise AI Knowledge Readiness • Integrate enterprise content sources including document stores, wikis, and cloud drives into the AI knowledge platform, ensuring content is accurate, fresh, and correctly scoped so AI agents only surface what they are authorized to access. • Set and enforce the quality bar for content ingestion across every integration, from freshness and accuracy to access-control correctness. Technical Leadership • Serve as technical lead across all three charters, making architectural calls, resolving ambiguity, and keeping the team focused on what ships. • Raise the engineering bar through code reviews, design reviews, and technical mentorship. What We Need To See: • Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience). • 8+ years of experience building and operating large-scale enterprise platforms, with demonstrated growth in technical scope and ownership. • Strong foundation in backend systems, distributed systems, and data engineering, including large-scale data processing, indexing pipelines, and systems built for reliability and scale. • Experience building or integrating data connectors or enterprise SaaS integrations (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar). • Experience training and evaluating ML models for classification tasks, particularly in security, content sensitivity, or information governance domains. • Strong communication skills with the ability to translate complex technical tradeoffs into clear recommendations for non-technical stakeholders. • Comfortable making calls with incomplete information and adjusting quickly as priorities shift. Ways to Stand Out From The Crowd: • Familiarity with access control models, remediation workflows, and audit requirements in enterprise security or compliance contexts. • Experience with AI/LLM data pipelines, vector stores, or RAG architectures, especially where access control fidelity is a hard requirement. • Hands-on experience with Databricks for audit logging and RBAC, or familiarity with Glean or similar enterprise search/DLP products. • Track record of leading platform migrations, tenant consolidations, or governance modernization at scale. • Experience mentoring engineers or leading cross-functional projects that required alignment across Security, Legal, Finance, and platform teams to ship. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most intelligent and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 8, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Sensitive Information Protection
Own the roadmap for sensitive-information detection and remediation, working with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust.
Evaluate and integrate ML-based classification approaches to detect sensitive content across unstructured data at scale, and iterate on precision and recall as the data landscape evolves.
Governed Data Access at Scale
Design and implement export-control enforcement and long-term audit logging, including authorization checks, schema design, data masking, retention, and RBAC controls across every connected system.
Enterprise AI Knowledge Readiness
Set and enforce the quality bar for content ingestion across every integration, from freshness and accuracy to access-control correctness.
Technical Leadership
Serve as technical lead across all three charters, making architectural calls, resolving ambiguity, and keeping the team focused on what ships.
Raise the engineering bar through code reviews, design reviews, and technical mentorship.
Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience).
8+ years of experience building and operating large-scale enterprise platforms, with demonstrated growth in technical scope and ownership.
Strong foundation in backend systems, distributed systems, and data engineering, including large-scale data processing, indexing pipelines, and systems built for reliability and scale.
Experience building or integrating data connectors or enterprise SaaS integrations (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar).
Experience training and evaluating ML models for classification tasks, particularly in security, content sensitivity, or information governance domains.
Strong communication skills with the ability to translate complex technical tradeoffs into clear recommendations for non-technical stakeholders.
Comfortable making calls with incomplete information and adjusting quickly as priorities shift.
Familiarity with access control models, remediation workflows, and audit requirements in enterprise security or compliance contexts.
Experience with AI/LLM data pipelines, vector stores, or RAG architectures, especially where access control fidelity is a hard requirement.
Hands-on experience with Databricks for audit logging and RBAC, or familiarity with Glean or similar enterprise search/DLP products.
Track record of leading platform migrations, tenant consolidations, or governance modernization at scale.
Experience mentoring engineers or leading cross-functional projects that required alignment across Security, Legal, Finance, and platform teams to ship.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 8, 2026.

Nice-to-haves

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aimlllm

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Benefits

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