Nvidia Data Analytics Applied AI Engineer- DFT Methodology 

India, Karnataka, Bengaluru 

719577000

What you'll be doing:

• As an integral member in our team, you will work on exploring Applied AI solutions for DFX and VLSI problem statements.

• Architect end-to-end generative AI solutions with a focus on LLMs, RAGs & Agentic AI workflows.

• Work on deploying predictive ML models for efficient Silicon Lifecycle Management of NVIDIA's chips.

• Collaborate closely with various VLSI & DFX teams to understand their language-related engineering challenges and design tailored solutions.

• Partner closely with cross-functional AI teams to provide feedback and contribute to the evolution of generative AI technologies.

• Work closely with DFX teams to integrate Agentic AI workflows into their applications and systems and stay abreast of the latest developments in language models and generative AI technologies.

• Define how data will be collected, stored, consumed and managed for next-generation AI use cases.

• You will also help mentor junior engineers on test designs and trade-offs including cost and quality.

What we need to see:

• BSEE or MSEE from reputed institutions with 2+ years of experience in DFT, VLSI & Applied Machine Learning

• Experience in Applied ML solutions for chip design problems

• Significant experience in deploying generative AI solutions for engineering use cases

• Good understanding of fundamental DFT & VLSI concepts - ATPG, scan, RTL & clocks design, STA, place-n-route and power

• Experience in application of AI for EDA-related problem-solving is a plus

• Excellent knowledge in using statistical tools for data analysis & insights

• Strong programming and scripting skills in Perl, Python, C++ or TCL desired

• Strong organization and time management skills to work in a fast-pace multi-task environment

• Self-motivated, independent, ability to work independently with minimal day-to-day direction

• Outstanding written and oral communication skills with the curiosity to work on rare challenges