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Program and Operations Engineer – Silicon Co-Design Group

NVIDIANVIDIA·Semiconductors

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About the 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 great 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’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA Silicon Co-Design Group wants an experienced Program & Operations Lead to act as the team’s operational multiplier. Your duties include managing the quality, accuracy, and timeliness of operational data covering contractor management, financials, and lab programs. You will present that data as clear options and recommendations so leadership can make well-informed decisions. You maintain the operational framework that keeps programs moving forward, prevents anything from being overlooked, and helps the team respond swiftly without missing critical details.

What you’ll be doing:

  • Own contractor lifecycle operations from start to finish. Engage directly with vendor program managers on candidate shortlisting, onboarding documents, and onboarding execution. Maintain an accurate live roster and send all approvals to leadership.

  • Collate and coordinate all team spending requests, capturing priority, justification, and budget impact. Manage the approvals workflow and maintain a real-time tracker of forecasted versus actual spend across categories.

  • Program-manage lab buildouts end-to-end in partnership with Real Estate & Site Services — owning project plans, achievement tracking, and cross-functional coordination across power, cooling, networking, safety, and equipment delivery.

  • Manage vendor relationships for lab equipment, services, and tooling — tracking deliverables, SLAs, and invoices; gathering performance data and presenting options to leadership for renewals, blocking issues, or vendor changes.

  • Proactively surface blockers, risks, and schedule changes across all workstreams — always with impact context and resolution options ready for leadership’s decision.

  • Develop and sustain operational dashboards for contractor, spend, and laboratory initiatives. Define and track key metrics for each workstream. Deliver structured analyses presenting clear choices and a suggested course of action at every critical decision point to support leadership in making informed decisions.

What we need to see:

  • Bachelor’s degree or equivalent experience in Engineering, Business, or Operations Management, accompanied by over 3+years in program management, business operations, or a data-focused operations position within a technology or semiconductor firm.

  • Demonstrated experience managing contractor operations — SOWs, POs, billing, onboarding — alongside strong financial acumen in budget tracking, approvals workflows, and planned vs. actual reporting.

  • Experience coordinating with external vendors and collaborating with Real Estate & Site Services teams on infrastructure projects such as lab buildouts or space fit-outs.

  • Strong analytical skills — able to define and track important metrics, work with operational data, identify trends, and translate complex information into crisp, decision-ready options for senior collaborators.

  • Proficiency with program management tools (JIRA, Smartsheet, or equivalent), data and reporting tools (Excel, Tableau, Power BI, or equivalent), and financial systems (Coupa, SAP, Ariba, or equivalent).

  • Proficiency with AI efficiency tools (Claude, Copilot, ChatGPT, or equivalent) to accelerate analysis, reporting, drafting, and operational workflows.

Ways to stand out from the crowd:

  • Prior experience in a Strategy & Operations, Business Operations, or Program Management role where converting complex operational data into leadership-ready recommendations was fundamental to the position.

  • Background supporting an engineering lab or hardware team in a semiconductor company, with familiarity with lab infrastructure and operations.

  • Ability to operate independently on execution while knowing exactly when and how to raise issues. Has a proven record of staying ahead of program risks and sharing the right information at the right time without prompting.

  • Demonstrated use of AI tools to meaningfully improve personal efficiency — faster analysis, automated reporting, smarter drafting, or streamlined workflows.

#LI-Hybrid

Skills & Tags

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Aplyr's read

NVIDIA is a pioneering force in GPUs and AI, attracting top talent in engineering and innovation-driven roles across various tech domains.

Synthesized from recent postings & public sources

What's promising

  • NVIDIA leads the GPU market, crucial for gaming and AI applications.
  • The company invests heavily in AI and deep learning, driving technological advancements.
  • NVIDIA's strong market position offers stability and growth opportunities for employees.

What to watch

  • High competition in the semiconductor industry can impact market share.
  • Rapid technological changes require constant adaptation and learning.
  • Intense workload and high expectations may affect work-life balance.

Why NVIDIA

  • NVIDIA's GPUs are industry benchmarks in gaming and professional graphics.
  • The company's AI research is at the forefront of deep learning innovation.
  • NVIDIA's culture emphasizes cutting-edge technology and engineering excellence.

Aplyr’s read is generated by AI from public sources. Was it useful?

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About NVIDIA

NVDA$200.04-4.13%

NVIDIA is a leading technology company known for its graphics processing units (GPUs) for gaming and professional markets, as well as its advancements in artificial intelligence and deep learning.

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