Applied Machine Learning Engineer
NVIDIA
About the Role
NVIDIA is defining the next era of computing through AI, where GPUs act as the brains for computers, robots, and self-driving cars. The Applied Machine Learning Engineer, AI for VLSI Design role is an integral part of the Circuit Solutions Group, building AI-driven software systems for Circuit Design. This involves combining automation algorithms, DL models, and agentic workflows to accelerate end-to-end design automation.
Key Responsibilities
- Work within a multi-functional team on various projects involving Pre-silicon and Post Silicon custom circuit design and related data, Circuit/Layout Optimization, and Spice correlation.
- Research and implement techniques on frontier solutions of electronic design automation.
- Build and innovate agentic AI solutions for VLSI design problems.
- Analyze problems or datasets, raise and validate hypotheses, design, and build models and algorithms until they reach the desired QOR.
Requirements & Tech Stack
- Experience in:
- Combinatorial Optimization
- Agentic AI and large language models
- Machine Learning for Chip Design & EDA
- Background in Algorithms/Data Structures.
- Experience in Applied Math/Machine Learning/Software programming.
- Shown ability in writing code in Python, C++.
Qualifications
- MS or PhD in Electrical/Computer Engineering degree (or equivalent experience).
Ways To Stand Out From The Crowd
- Prior background in large-scale EDA software development is a plus.
- Prior experience in CMOS layout drawing, including schematic-to-layout translation and DRC/LVS compliance, is a definite plus.
- Enjoy working with multiple levels and teams across organizations (engineering/research, product, sales and marketing teams).
- Effective verbal/written communication, and technical presentation skills.
Note: Applications will be accepted at least until October 3, 2026.
Note: NVIDIA uses AI tools in its recruiting processes.