Analyze full‑chip and unit‑level power using internal and industry‑standard RTL and gate‑level power tools, and translate data into concrete design and architectural improvements.
Develop and productionize power‑aware models and flows, including ML/RL‑based techniques for anomaly detection, dynamic power management, and design‑space exploration.
Design and train new LLMs that “learn the art” of power analysis from design data, power reports, bug histories, and best practices—so they can:
Assist engineers in interpreting complex power data
Propose likely root causes and candidate fixes
Recommend architectural and micro‑architectural optimizations for power
Perform comparative power analysis across workloads, products, and design options to identify trends, anomalies, and optimization opportunities that aren’t obvious from first principles alone.
Partner closely with Architects, Performance, Software, ASIC Design, and Physical Design teams to interpret power data, root‑cause power bugs, and drive fixes and design changes.
Prototype and evaluate new architectural features in Verilog, with a strong focus on their power and energy implications.
Automate and scale flows (Python/Perl/C++), and define new pipelines that fast‑track power anomaly detection and close the loop between power data, AI models, and design decisions.
Apply AI to power optimization: build and deploy data‑driven models—using machine learning, reinforcement learning, data analytics, and custom LLMs—to recommend or automatically tune power‑efficient configurations and policies.

Requirements

MS (or equivalent experience) and 5yrs experience OR PHD + 3yr experience in EE/CE/CS or related fields.
Strong understanding of energy consumption, power estimation, data movement, and low‑power design.
Familiarity with Verilog and ASIC design principles, and hands‑on experience with tools such as PowerArtist, PrimePower/PrimePower RTL, RTL Architect, or similar.
Solid coding and automation skills, preferably in Python, Perl, and C++.
Experience or strong interest in machine learning, reinforcement learning, and data analytics, ideally applied to EDA, architecture, or system‑level optimization.
Interest or experience in building and using LLMs or other foundation models as engineering copilots—especially for EDA/power/architecture workflows.
Excellent communication and collaboration skills to work effectively with cross‑functional design and architecture teams.
A genuine desire to bring data‑driven, AI‑assisted decision‑making into power architecture and help shape the energy profile of NVIDIA’s future products.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

power analysispower estimationlow-power designmachine learningreinforcement learningdata analyticsVerilogcodingautomationpower-aware models

Soft Skills

communicationcollaborationproblem-solvinginterpretation of complex dataroot-cause analysisdesign optimizationcross-functional teamworkdata-driven decision-makingengineering copilotarchitectural improvements

Certifications

MS in EE/CE/CSPhD in EE/CE/CS