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NVIDIA

Designs the chips and software that power AI

Senior Silicon Product Definition Engineer - Silicon Co-Design Group

Hardware EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedHybrid

About the Job

Requirements

  • A Bachelor of Science or Master of Science degree in Electrical Engineering, Electronics Engineering, or Systems Engineering.
  • At least 5 years of experience in silicon product definition, post-silicon characterization, product engineering, or performance and power analysis for GPU, CPU, SoC, or other complex silicon products.
  • Hands-on experience with silicon bring-up and performance-per-watt analysis, including optimization across process, voltage, and temperature conditions.
  • Strong programming and data-analysis skills using Python and one or more of Perl, JMP, SQL, or equivalent tools, with practical knowledge of statistics and large-dataset manipulation.
  • A strong technical foundation in digital design, circuit analysis, computer architecture, critical-path analysis, process technology, and transistor/device physics.
  • Working knowledge of firmware, BIOS, drivers, operating-system behavior, and software applications that influence silicon performance and power.
  • Ability to drive ambiguous technical issues to root-cause closure across globally distributed, multifunctional teams.
  • Strong written and verbal communication skills, including the ability to translate complex technical issues into clear, decision-ready options for executive leadership.

Responsibilities

  • Own product-definition analysis across performance, power, yield, and quality, using large silicon datasets to identify product opportunities, risks, and trade-offs.
  • Drive product-specific configurations by isolating frequency, power, and thermal-limiting constraints and translating findings into silicon, firmware, BIOS, driver, and platform changes.
  • Build statistical models, automation, and visualization workflows that improve decision quality.
  • Partner with Tools and Methodology teams to move data modeling, test procedures, and closed-loop analysis infrastructure from post-silicon learning into future programs.
  • Lead multifunctional root-cause closure with architecture, design, validation, software/firmware, applications engineering, manufacturing, and operations teams from bring-up through production.
  • Evaluate performance-per-watt, process-voltage-temperature behavior, yield, and quality trade-offs, and drive productized fixes through implementation and confirmation.
  • Translate complex silicon and system signals into concise, decision-ready options that provide executive leadership with clarity on product configuration, performance targets, and program-impacting risks.
  • Own and deliver BIOS-level VF settings across thermal, power, and frequency domains, debug issues, and drive cross-functional fixes to close power, thermal, performance, yield, and quality gaps.

Desired Qualifications

  • A track record of defining and shipping product configurations that measurably improved performance, power efficiency, yield, quality, or time to market.
  • Experience with advanced process nodes and high-complexity GPU, CPU, AI accelerator, automotive, or datacenter products.
  • Experience building reusable product-definition methodologies, statistical models, or automation adopted across multiple silicon programs.
  • Proficiency with AI productivity tools such as Claude, Copilot, ChatGPT, or equivalent tools to accelerate analysis, debugging, drafting, and decision-making workflows, with clear outcomes and appropriate guardrails.
  • Comfort navigating ambiguity, building technical structure in fast-moving silicon programs, and collaborating with global teams from diverse cultural backgrounds.

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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