Senior Applied Machine Learning Engineer - VLSI Design

NVIDIA•Santa Clara, CA

1d•$152,000 - $264,500•Hybrid

About The Position

NVIDIA is seeking a Senior Applied Machine Learning Engineer to join a team that builds AI-driven software systems for circuit design. This role combines automation algorithms, deep learning models, and agentic workflows to accelerate end-to-end design automation. The engineer will work on projects involving pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI systems for EDA/design automation. Applications include silicon data analysis, manufacturing process variation analysis, VLSI circuit design, timing, and agent-driven design exploration and optimization.

Requirements

MS/PhD in Electrical/Computer Engineering, Computer Science, Applied Mathematics, or equivalent experience.
4+ years experience in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design is required.
Prior experience in Applied Math/ML/Software programming with proven ability in writing code in Python and C++.
Experience with deep learning algorithms, AI agent frameworks, and tools such as PyTorch, LangChain, or LangGraph is a definite plus.

Nice To Haves

Experience building AI systems for EDA, design automation, or circuit design workflows.
Research or project experience in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems.
Experience building agentic systems, autonomous optimization loops, self-improving AI systems, or production-scale AI/ML platforms.
Effective verbal/written communication and technical presentation skills.
Self-starter with passion for growth, continuous learning, and sharing findings across the team.

Responsibilities

Work within a multi-functional team on projects involving pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI systems for EDA/design automation.
Work on applications ranging from silicon data analysis, manufacturing process variation analysis, VLSI circuit design, timing, and agent-driven design exploration and agent flow optimization.
Translate requirements into data science, AI/ML, and agentic system problems; architect and build solutions.
Test and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools within the organization.
Analyze datasets, raise and validate hypotheses, extract relevant features, and build models and self-improving workflows on top of them.
Optimize models, algorithms, and autonomous optimization systems until they reach the desired QOR.