Full-Time

Principal Engineer

Deadline 7/28/26
NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$248k - $391k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

In Person

Bachelor's, Master's

Category
Software Engineering (1)
Required Skills
SharePoint
Role-based Access Control
Confluence
Data Governance
Data Analysis

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Requirements
  • 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
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-vs-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 (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
  • 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
  • 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 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 designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • NVIDIA reported $215.9 billion fiscal 2026 revenue, up 65%, on February 25, 2026.
  • The August 10, 2026 Wall Street financing pact opens more buyers for NVIDIA hardware.
  • Nemotron 3.5 Lightning boosts ecosystem lock-in while driving cheap GPU demand.

What critics are saying

  • US Commerce tightened China chip controls again on May 31, 2026.
  • The $500 billion financing push ties growth to GPU resale values and customer defaults.
  • An AI hardware glut from AMD, Huawei, or Chinese foundries crushes collateral and pricing.

What makes NVIDIA unique

  • CUDA remains the default software moat for AI training and deployment.
  • NVIDIA secured SK Hynix as its largest memory partner in June 2026.
  • Vera Rubin and Blackwell keep NVIDIA ahead in rack-scale AI systems.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-2%

2 year growth

-3%
CNBC
Aug 12th, 2026
Nvidia taps Wall Street to raise $500B for AI infrastructure buildout

Nvidia has partnered with six US asset managers willing to raise $500 billion to finance AI infrastructure development. The chip giant is positioning AI infrastructure as a new asset class, with the plan hinging on GPUs retaining value over time like traditional hard assets rather than depreciating electronics. The approach carries risks. Ben Emons of FedWatch Advisors warned that Chinese manufacturers could flood markets with low-cost chips, potentially causing hardware prices to collapse and eroding collateral backing billions in private loans. Nvidia also launched Nemotron 3.5 Lightning, its first open-source AI model since CEO Jensen Huang advocated for open models. The lightweight model runs on a single GPU, potentially boosting chip sales by offering cheaper alternatives to proprietary models. Meanwhile, oil prices rose over 6% this week as prospects dimmed for a deal to increase traffic through the Strait of Hormuz.

Yahoo Finance
Aug 11th, 2026
Musk's 10GW SpaceX data centre plan could generate $300B in Nvidia orders or expose dangerous concentration risk

Elon Musk has announced plans to scale SpaceX data centres from 1.4 gigawatts to 10 gigawatts by 2027, working exclusively with NVIDIA hardware. Research firm SemiAnalysis estimates this could generate $150 billion to $500 billion in capital spending. The move could push NVIDIA shares towards $500, building on its $5.42 trillion market capitalisation and 92% data centre revenue share. NVIDIA recently announced a $500 billion financing partnership with Apollo, BlackRock, and other major firms to support AI infrastructure buildouts. However, the proposal creates significant concentration risk. SpaceX would propose capital expenditure rivalling Amazon Web Services and Google combined, whilst being far less profitable. NVIDIA already holds $119 billion in supply commitments. If SpaceX funding tightens or hyperscale customers slow orders, the stock could face substantial downside risk. Meanwhile, AMD has surged 121% year-to-date versus NVIDIA's 17% gain.

Cointime
Aug 11th, 2026
AI startup Trajectory raises $40M at $300M valuation led by Sequoia Capital

AI infrastructure startup Trajectory has raised $40 million at a $300 million post-money valuation, led by Sequoia Capital with participation from Nvidia and Bessemer, according to The Information. The funding comes just two months after the company secured a $15 million seed round at a $115 million valuation. Founded in May by former Google DeepMind researchers Ronak Malde and Michael Elabd, alongside ex-Apple researcher Arjun Karanam, Trajectory focuses on continuous learning technology. The platform transforms user corrections, retries and edits into training signals, enabling AI models to improve after deployment. The company automates this process, allowing enterprises to continuously adjust models, prompts and harnesses based on real usage data. Clay, Decagon and Harvey are currently using or testing the technology.

Yahoo Finance
Aug 11th, 2026
Nvidia develops Nemotron 4 open-source AI model with 1T+ parameters

Nvidia is developing Nemotron 4, a new AI model family aimed at rivaling top open-source models globally, The Information reported. The largest model is expected to have at least 1 trillion parameters, according to employees working on the project. Nvidia has not set a release date, though the model could be ready as early as late autumn. The company has yet to complete final training. Separately, Nvidia unveiled Nemotron 3.5 Lightning for tasks including code review and security monitoring. It also released NeMo Switchyard, an open-source model-routing library. The chip giant is among few major US firms releasing open-source models, which have gained attention as AI costs rise and Chinese models approach capabilities of systems from Anthropic and OpenAI.

CNBC
Aug 11th, 2026
Nvidia releases first open-source AI model after CEO Huang's open letter debut

Nvidia has released Nemotron 3.5 Lightning, its first open-source AI model since CEO Jensen Huang entered the open-source AI debate. The model was developed particularly for autonomous AI agents and will be available on HuggingFace and Nvidia's website. Huang previously argued that open-weight models allow companies greater control, spur competition, and bring down pricing. For Nvidia, open-source AI boosts chip sales, as the models still require GPUs to run. Companies including CodeRabbit and Harvey have tested the model. Nvidia also released NeMo Switchyard software to determine the most appropriate and cost-effective AI model for specific tasks. Nvidia used distillation techniques to give Nemotron 3.5 Lightning capabilities similar to its larger models.