Applied AI Engineer - DFT Methodology

Job Title

Applied AI Engineer - DFT Methodology

Role Summary

Develop and deploy applied AI and machine-learning solutions addressing DFT/DFX and VLSI engineering problems. Architect end-to-end generative AI solutions (LLMs, RAGs, agentic workflows) and predictive models to improve silicon lifecycle management.

Collaborate with VLSI, DFX, and cross-functional AI teams to integrate AI-driven workflows, define data collection and management approaches, and mentor junior engineers on test design trade-offs.

Experience Level

Mid-level — requires approximately 2+ years of relevant industry experience in DFT, VLSI and applied machine learning.

Responsibilities

Primary responsibilities include designing and delivering AI solutions for semiconductor design and test challenges, and integrating those solutions into engineering workflows.

• Explore and design applied AI/ML solutions for DFX and VLSI problem statements.
• Architect end-to-end generative AI solutions focused on LLMs, retrieval-augmented generation, and agentic AI workflows.
• Deploy predictive ML models for silicon lifecycle management and production-support use cases.
• Collaborate with VLSI and DFX teams to understand language-related engineering challenges and deliver tailored solutions.
• Integrate agentic AI workflows into existing applications and systems.
• Define data collection, storage, consumption, and management strategies for AI use cases.
• Mentor and provide guidance to junior engineers on test designs, cost/quality trade-offs, and engineering best practices.

Requirements

Must-have technical skills, experience, and attributes.

• Practical experience applying ML/AI solutions to chip-design or VLSI engineering problems.
• Experience deploying generative AI solutions for engineering use cases (LLMs, RAG, agentic workflows).
• Solid understanding of DFT and VLSI fundamentals: ATPG, scan methodologies, RTL and clock design, STA, place-and-route, and power concepts.
• Proficiency with statistical tools for data analysis and deriving insights from engineering data.
• Programming and scripting skills in one or more of: Python, C++, Perl, TCL.
• Strong organization, time-management, self-motivation, and ability to work independently in a fast-paced environment.
• Excellent written and verbal communication skills and curiosity for complex, uncommon technical challenges.

Nice-to-have:

• Experience applying AI to EDA problems.

Education Requirements

Bachelor of Science in Electrical Engineering (BSEE) or Master of Science in Electrical Engineering (MSEE) from a reputable institution. The posting specifies 2+ years of relevant experience in DFT, VLSI and applied machine learning alongside these degrees.

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.