AI and ML Power Methodology Engineer

Job Title

AI and ML Power Methodology Engineer

Role Summary

Develop AI/ML-driven methodologies and tools to estimate pre-silicon power and improve GPU energy efficiency. Work on the Power Team in close collaboration with hardware, ML, and infrastructure engineers to measure, analyze, and optimize power for graphics and AI workloads.

Experience Level

Mid-level — requires 5+ years of relevant industry experience.

Responsibilities

Primary responsibilities include research and tool development to enable data-driven power estimation, analysis, and optimization.

• Research, develop, and own AI/ML/DL methodologies for pre-silicon power estimation and GPU energy-efficiency improvements.
• Develop data pipelines to gather power data from silicon, emulation, and other sources for model training and analysis.
• Build tools for dataset creation, annotation, and management for training LLMs and other models.
• Design and implement training and fine-tuning workflows for large language models, RAG pipelines, vector databases, and agentic frameworks.
• Use LLMs and analytics to analyze power patterns, generate optimized code, and provide actionable debugging and optimization insights.
• Create user-facing visualizations and interfaces to present power-analysis results and support decision-making.
• Ensure efficient storage and retrieval of large domain-specific datasets.

Requirements

Must-have technical skills and experience.

• Minimum 5+ years of relevant experience in software, ML, or power methodology roles.
• Proficiency in rapid prototyping with Python and C++; strong software engineering fundamentals, data structures, and algorithms.
• Familiarity with training and fine-tuning large language models, advanced RAG pipelines, vector databases, and agentic frameworks.
• Ability to formulate and analyze algorithms, including runtime and memory complexity considerations.
• Experience building data pipelines and tools for collecting, annotating, and processing domain-specific datasets.
• Strong verbal and written communication and interpersonal skills.
• Quantitative, data-driven approach to decision-making and optimization.

Education Requirements

MS or PhD in a related technical field (for example, Computer Science, Electrical Engineering) or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.