Nvidia Applied Machine Learning Engineer - Circuit Design 

United States, Texas 

421450870

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

What you'll be doing:

• 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

• Work on projects with applications ranging from analysis of silicon data, manufacturing process variation analysis, VLSI circuit design and timing etc.

• Responsible for translating the requirements into a data science problem, architect and build solutions.

• In charge for testing and release of models that integrate with existing machine learning and visualization tools within the organization.

• Responsible for analyzing the datasets, raise and validate hypotheses, extract relevant features and build models on top of them.

• Optimize the models and algorithms until they reach the desired QOR.

What we need to see:

• MS with 3+ years experience or PhD with 1+ years experience in Electrical/Computer Engineering

• Experience with VLSI, Circuit Design, CMOS Device Physics, Timing, ASIC, EDA is a strict requirement for this role

• Experience in Applied Math/ML/Software programming with proven ability in writing code in Python and C++ is required

• Prior experience in CMOS layout drawing, including schematic-to-layout translation and DRC/LVS compliance, is a definite plus

• Experience with ML/DL algorithms with frameworks such as TensorFlow, PyTorch, Spark is a plus

Ways to stand out from the crowd:

• Enjoy working with multiple levels and teams across organizations(engineering/research,product, sales and marketing teams)

• Effective verbal/written communication, and technical presentation skills

• Self-starter with passion for growth, real enthusiasm for continuous learning and sharing findings across the team