Full-Time

Silicon Power Engineer

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

No salary listed

Bengaluru, Karnataka, India

Hybrid

Hybrid role; some on-site days in Bengaluru.

Bachelor's, Master's

Category
Hardware Engineering (1)
Required Skills
Data Analysis

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Requirements
  • B. Tech or Master of Technology in Electronics Engineering, with 2+ years related work experience.
  • Strong understanding of aspects related to silicon power and performance, technology node impacts, Hardware and Software interactions at system level.
  • Hands-on experience with silicon bring up, validation and productization, good knowledge in board and system design considerations, Power supply design.
  • Very good problem solving and hardware debugging skills, very good data analysis and logical reasoning skills.
  • Strong familiarity with hardware laboratory environment and understanding of various lab equipment.
  • Experience in working with Windows.
Responsibilities
  • Perform test case execution, debug silicon issues related to correlation and functionality, generate high quality results and provide design feedback.
  • Find creative solutions to sophisticated silicon and system level problems and be on the frontline to lead show-stopper bugs, to enable product shipment.
  • Work alongside system architects, chip and board designers, software/firmware engineers, HW/SW applications engineers, process/reliability specialists, ATE engineers, and operations in a dynamic & high-energy work environment to bring industry-defining products to market.
  • Collaborate with cross-functional teams to craft essential next generation product features that are important for performance, power optimization, and power management.
  • Collaborate to craft tools for post-silicon work, build post-silicon method‐ ologies to characterize silicon power, correlate silicon behaviour with simulation
  • Work with various Arch & Design teams to come up with test plans of new features.
  • Collaborate with other validation &bring up teams to bring up/characterize silicon power and power saving features.
  • Work with design & estimation teams to correlate with pre-silicon expectation, work with HW and SW teams to do the vital tuning and optimization of silicon power.
  • Develop power consumption models to be used in binning, productization and customer application notes, characterize and develop various power control mechanisms together with Arch/Design/SW teams.
Desired Qualifications
  • Linux exposure is highly preferred.
  • Working experience with scripting languages like perl and/or python is a plus point.
  • Exposure to critical path analysis, power analysis, process technologies, transistor/device physics, silicon reliability and aging mechanisms.
  • Background with power supply and substrate noise analysis and mitigation. Exposure to digital design, circuit analysis, computer architecture, BIOS, drivers, and software applications.
  • Must be a great teammate and ready to collaborate with global teams from diverse cultural backgrounds in a high energy environment.

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.