Full-Time

Hardware Engineer

Silicon Power and Productization

Updated on 9/3/2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

No salary listed

Bengaluru, Karnataka, India

Hybrid

Bachelor's, Master's

Category
Hardware Engineering (1)
Required Skills
Microsoft Windows
Machine Learning
Linux/Unix
Data Analysis

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Requirements
  • A Bachelor of Science or Master of Science degree in Electronics Engineering, Electrical Engineering, or a related field, or equivalent experience.
  • At least 2 years of experience in silicon bring-up, characterization, validation, productization, or a related hardware field.
  • Understanding of silicon power and performance, including process technology, voltage, frequency, workloads, and operating conditions.
  • Experience with system-level hardware debugging and interactions across silicon, board hardware, firmware, and software.
  • Data analysis and problem-solving skills, including the ability to turn measurements into hypotheses and design experiments to test them.
Responsibilities
  • Drive silicon power productization from pre-silicon planning through bring-up and production, including test strategy, feature readiness, characterization, and optimization.
  • Partner with architecture and design teams to identify improvements, validate features, and help translate them into production-ready solutions.
  • Correlate measured silicon behavior with pre-silicon expectations, investigate gaps, and drive complex issues to root cause.
  • Build power and performance models and characterization methodologies that decide silicon binning, product specifications, productization decisions, and customer guidance.
  • Use artificial intelligence, machine learning, and data-driven methods to analyze characterization and telemetry data, identify anomalies and trends, and accelerate issue debugging across silicon, board, power delivery, firmware, and software.
Desired Qualifications
  • Experience with Windows and Linux systems, low-power states, power controllers, silicon power, device physics, or power delivery.
  • Contributions that improved silicon bring-up, characterization coverage, pre- and post-silicon correlation, debug efficiency, or productization methodology.
  • Experience with power modeling, silicon binning, and productization.
  • Experience applying artificial intelligence and machine learning to engineering workflows, such as debug assistants, characterization analytics, or automated anomaly and trend detection.

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

  • Amazon ordered 2 million GPUs for 2027-2028, validating hyperscaler demand.
  • August 2026 Lancium investment secured 4 GW leased capacity and 15 GW pipeline.
  • Blackwell Ultra and Vera Rubin shipments began; production ships in late 2026.

What critics are saying

  • Taiwan prosecutors indicted NVIDIA staff August 24, 2026 over illegal China server exports.
  • China’s antitrust probe still threatens fines, remedies, and slower mainland sales.
  • China export controls and ASIC substitution can cut NVIDIA off from a trillion-dollar market.

What makes NVIDIA unique

  • CUDA and NVLink lock developers into NVIDIA’s full-stack AI platform.
  • FY2026 revenue hit $215.9 billion, with data center revenue $193.7 billion.
  • Rubin launched February 2026, targeting 10x lower inference costs than Blackwell.

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Benefits

Company Equity

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Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-1%

2 year growth

-2%
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