Senior Software Engineer - Enterprise Data

NVIDIA•Santa Clara, CA

9d•$168,000 - $322,000

About The Position

NVIDIA is hiring an engineer to build its Enterprise Data Governance platform. The platform protects sensitive information, provides governed access to data across the company’s collaboration and content systems, and supports the knowledge infrastructure needed to make enterprise AI trustworthy and accurate. The role addresses the challenge of enabling AI agents to access useful enterprise knowledge while enforcing precise, auditable controls required by export controls, legal constraints, and security policies. The engineer will lead work across sensitive-information protection, governed data access, enterprise AI knowledge readiness, and technical leadership. This includes shaping detection and remediation capabilities, rolling out integrations and access controls across enterprise systems, ensuring content ingestion is accurate and correctly scoped, and making architectural decisions while mentoring engineers.

Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
At least 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 designed for reliability and scale.
Experience building or integrating data connectors or enterprise SaaS integrations, such as Confluence, SharePoint, Google Drive, Slack, or Teams.
Experience training and evaluating ML models for classification tasks, particularly in security, content sensitivity, or information governance.
Strong communication skills, including the ability to translate complex technical tradeoffs into clear recommendations for non-technical stakeholders.
Comfort making decisions with incomplete information and adjusting quickly as priorities shift.

Nice To Haves

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 requiring alignment across Security, Legal, Finance, and platform teams.

Responsibilities

Own the roadmap for sensitive-information detection and remediation, working with Finance, Legal, and Security on classification models, remediation workflows, and leadership reporting.
Evaluate and integrate ML-based classification approaches to detect sensitive content across unstructured data at scale, and improve precision and recall as the data landscape evolves.
Drive production rollout and self-service onboarding for the enterprise data access platform, including connectors for email, messaging, document stores, and search systems.
Design and implement export-control enforcement and long-term audit logging, including authorization checks, schema design, data masking, retention, and RBAC controls across connected systems.
Integrate enterprise content sources, including document stores, wikis, and cloud drives, into the AI knowledge platform while ensuring content is accurate, fresh, correctly scoped, and only surfaced to authorized users.
Set and enforce content-ingestion quality standards across integrations, covering freshness, accuracy, and access-control correctness.
Serve as technical lead across the three charters, make architectural decisions, resolve ambiguity, and keep the team focused on deliverables.
Raise engineering standards through code reviews, design reviews, and technical mentorship.