AI and ML Power Methodology Engineer

Job Title

AI and ML Power Methodology Engineer

Role Summary

Develop and own AI/ML methodologies and tools to estimate pre-silicon power and improve GPU energy efficiency. Work on data pipelines, model training, and tooling to analyze power across graphics and AI workloads.

Collaborate closely with hardware, ML, and infrastructure teams to create methods that guide architecture, design, and power management decisions for future NVIDIA AI solutions.

Experience Level

Mid-level. 5+ years of relevant experience.

Responsibilities

Primary technical responsibilities and deliverables:

• Research and implement AI/ML/DL methodologies for pre-silicon power estimation and energy-efficiency optimization.
• Design and build data pipelines to collect power data from silicon, emulation, and other sources.
• Create tooling and processes to gather, label, and prepare domain-specific datasets for model training.
• Develop training and fine-tuning workflows for large language models, including RAG pipelines and vector database integration.
• Implement LLM-based analysis tools to detect power patterns, suggest optimizations, and generate optimized code snippets.
• Enable efficient storage, retrieval, and maintenance of power-related data in databases.
• Produce user-facing visualizations and dashboards to surface insights and support debugging.
• Collaborate with hardware, ML, and infra teams to validate methods and integrate findings into design flows.

Requirements

Key qualifications and skills — must-haves first, followed by beneficial skills.

• Must-have: 5+ years of relevant industry experience.
• Must-have: Proficiency in Python and C++ and solid software engineering fundamentals (data structures, algorithms).
• Must-have: Experience with training/fine-tuning LLMs and building RAG pipelines and vector-database workflows.
• Must-have: Ability to formulate and analyze algorithms and reason about runtime and memory complexity.
• Must-have: Strong quantitative skills and experience applying analytics to engineering problems; good verbal and written communication.
• Nice-to-have: Prior experience with power estimation, GPU/ASIC design flows, emulation, or silicon measurement data.
• Nice-to-have: Experience building scalable data storage and retrieval systems, agentic frameworks, or visualization tooling.

Education Requirements

MS (or equivalent practical experience) or PhD in a related field; equivalent practical experience is explicitly acceptable.

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.