Senior Power Analysis and Optimization Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1

This role focuses on applying AI, ML, and LLMs to optimize power efficiency in NVIDIA's GPUs and SoCs. The engineer will develop and productionize ML/RL-based models for power analysis and optimization, design and train custom LLMs for interpreting power data and recommending improvements, and apply AI to tune power-efficient configurations. The role involves analyzing power data, partnering with cross-functional teams, and automating flows.

What you'd actually do

  1. Develop and productionize power-aware models and flows, including ML/RL-based techniques for anomaly detection, dynamic power management, and design‑space exploration.
  2. 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
  3. 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.
  4. 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.

Skills

Required

  • 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.

Nice to have

  • 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.

What the JD emphasized

  • advanced analytics and AI, including LLMs trained specifically for power analysis
  • Power Analysis and Optimization driven by AI and LLMs is the future
  • combining power architecture expertise with machine learning, reinforcement learning, data analytics, and large language models (LLMs)
  • Develop and productionize power-aware models and flows, including ML/RL-based techniques
  • Design and train new LLMs
  • Apply AI to power optimization: build and deploy data-driven models—using machine learning, reinforcement learning, data analytics, and custom LLMs

Other signals

  • Develop and productionize power-aware models and flows, including ML/RL-based techniques
  • Design and train new LLMs that “learn the art” of power analysis
  • Apply AI to power optimization: build and deploy data-driven models