Principal Engineer - Enterprise Content and AI Data Platform
NVIDIA is hiring a Principal Engineer - Enterprise Content and AI Data Platform in Santa Clara, United States. It pays $248k-$391k a year and Level rates it ; you can apply on NVIDIA.
Level's read on this job
- On Level's scale, this role is Uses AI ●●○○: an ordinary role that requires AI tools.
- The posting mentions Databricks, RAG and AI agents among its tools and skills.
- Level classes it as a lead role.
- NVIDIA has 2,700+ other open roles on Level.
AI in this role
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by incredible technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you will be immersed in a diverse, encouraging environment where everyone is inspired to do their best work.
NVIDIA is hiring a Principal Engineer to lead three interconnected and strategic platform charters at the intersection of information security, enterprise collaboration, and AI — forming the backbone of how NVIDIA protects sensitive content, governs enterprise data access, and powers the next generation of AI agents at scale. These platforms span sensitive-information protection and DLP, a central data governance layer that controls access to enterprise content across email, messaging, document stores, and search, and an enterprise AI knowledge platform that ingests and validates content for reliable consumption by AI agents — with a consistent focus on access control fidelity, export-control enforcement, and production readiness. This is a high-impact individual contributor role with a clear path into engineering leadership and management. We are looking for a technically strong engineer who is eager to grow beyond IC execution — someone who can own platform strategy, influence cross-functional stakeholders, and eventually step into leading and growing a team. Strong cross-functional partnership across Security, Legal, Finance, and AI platform teams is essential from day one.
What You Will Be Doing:
Sensitive Information Protection & Remediation
- Own the roadmap for sensitive-information detection and remediation — partnering with Finance, Legal, and Security to define classification models, remediation workflows, and reporting that leadership can trust.
- Lead build-vs-buy decisions and vendor evaluations to determine what to invest in internally versus augment with third-party DLP or search tools.
Enterprise Data Access & Governance
- 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. Evaluate third-party partners that could extend the platform for NVIDIA's customer-facing go-to-market motions.
Enterprise AI Knowledge Platform
- Drive integration of enterprise content sources — document stores, wikis, and cloud drives — into the AI knowledge platform, ensuring content is accurate, fresh, and access-controlled for agent consumption.
- Define production readiness gates and maintain a clear ownership boundary: this team owns integration correctness and quality, not full platform operations.
Leadership & Cross-functional Coordination
- Serve as the technical lead across all three workstreams, aligning stakeholders in Security, Finance, Legal, and AI platform teams and driving clarity on ownership and priorities.
- Mentor engineers and foster a culture of documentation, runbooks, and operational rigor; demonstrate readiness to grow into an engineering management role.
What We Need To See:
- Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience).
- 15+ years of experience building and operating large-scale enterprise platforms, with a track record of growing technical scope and influence beyond individual execution.
- Strong foundation in backend systems, distributed systems, and high-performance computing — experience with large-scale data processing, indexing pipelines, and systems designed for reliability and scale.
- Background in enterprise security, data governance, or compliance platforms — familiarity with classification, remediation workflows, access control models, and audit requirements is a plus.
- Experience building or integrating secure API platforms, data connectors, or enterprise SaaS integrations at scale (e.g., Confluence, SharePoint, Google Drive, Slack, Teams, or similar).
- Proven ability to drive cross-functional alignment across Security, Legal, Finance, and platform engineering teams.
- Excellent written and verbal communication skills; ability to translate complex technical tradeoffs into executive-level clarity.
- Demonstrated interest in growing into engineering leadership — experience mentoring peers, leading projects, or taking on informal leadership responsibilities is a strong signal.
- Comfortable holding ambiguity and driving decisions in a fast-paced, high-stakes environment.
Ways to Stand Out From The Crowd:
- Experience with AI/LLM data pipelines, vector stores, or RAG architectures — particularly in connecting enterprise content sources to AI platforms with strict access controls.
- Hands-on experience with Databricks or similar platforms for audit logging, data governance, and RBAC. Familiarity with Glean or similar enterprise search/DLP products and their integration patterns.
- Experience with vendor evaluation and build-vs-buy decisions for enterprise security or content platforms.
- Ability to leverage AI and agentic automation to drive operational efficiency and reduce engineering toil.
- Track record of leading platform migrations, tenant consolidations, or governance modernization efforts.
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 other objective job-related factors. The expected base salary range for this position is 248,000 USD – 391,000 USD.You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 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.How we rate this
Principal Engineer - Enterprise Content and AI Data Platform at NVIDIA rates 43 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.
Uses AI. An ordinary role that requires AI tools.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What are the limits of Databricks that you've run into, and how did you work around them?
- This role expects you to use AI tools as part of the job. Which ones have you used, and for what?
- Tell me about a time an AI tool got something wrong. How did you catch it?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.
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