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NVIDIA

Designs the chips and software that power AI

Principal Engineer - Enterprise Content and AI Data Platform

Backend EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A 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.
  • A strong foundation in backend systems, distributed systems, and high-performance computing, with experience in large-scale data processing, indexing pipelines, and systems designed for reliability and scale.
  • A 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, such as Confluence, SharePoint, Google Drive, Slack, Teams, or similar.
  • Ability to drive cross-functional alignment across Security, Legal, Finance, and platform engineering teams.
  • Excellent written and verbal communication skills and 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.
  • Comfort holding ambiguity and driving decisions in a fast-paced, high-stakes environment.

Responsibilities

  • 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-versus-buy decisions and vendor evaluations to determine what to invest in internally versus augment with third-party DLP or search tools.
  • Drive production rollout and self-service onboarding for the enterprise data access platform, spanning connectors across email, messaging, document stores, and search, including Outlook, Teams, Slack, Confluence, OneDrive, SharePoint, Google Drive, and Glean.
  • Design and implement export-control enforcement and long-term audit logging, including authorization checks, schema design, data masking, retention, and role-based access control (RBAC).
  • Evaluate third-party partners that could extend the platform for NVIDIA's customer-facing go-to-market motions.
  • Drive integration of enterprise content sources, including 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: the team owns integration correctness and quality, not full platform operations.
  • 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.

Desired Qualifications

  • Experience with AI/LLM data pipelines, vector stores, or retrieval-augmented generation (RAG) architectures, particularly 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-versus-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.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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