Senior Applied Machine Learning Engineer, VLSI Design

Nvidia

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Salary: $152,000–$230,000 / yr
Posted: 30 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $212k

This role $191k

$139k most similar roles pay here $276k

This role pays less than 62% of similar roles. Most pay $177,250–$246,150 — the shaded band above. At the midpoint, this role pays about $191k versus about $212k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Applied Machine Learning Engineer, VLSI Design

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As a Senior Applied Machine Learning Engineer - VLSI Design, you will join a multi-functional team building AI-driven software systems for circuit design. You will translate complex requirements into data science and machine learning problems to develop automation algorithms, deep learning models, and agentic workflows. Your daily work involves analyzing pre-silicon and post-silicon hardware data, performing circuit optimization, and managing SPICE correlation. You will build and test models that integrate with existing design automation and visualization tools while optimizing autonomous systems for quality of results. The role requires expertise in Python, C++, PyTorch, LangChain, or LangGraph to solve problems in silicon data analysis, manufacturing process variation, and timing. This position focuses on the technical challenge of accelerating end-to-end design automation through agent-driven exploration and self-improving workflows within the specialized domain of VLSI circuit design.

Skills

Python C++ PyTorch LangChain LangGraph Deep Learning Machine Learning EDA VLSI ASIC Circuit Design SPICE Data Science Agentic Workflows Design Automation QOR Silicon Analysis Automation Algorithms

What you'll do

Develop AI systems and automation algorithms for circuit design and EDA workflows.
Translate hardware requirements into data science, machine learning, and agentic system problems.
Architect and build solutions for silicon data analysis and manufacturing process variation.
Analyze datasets to extract features and build self-improving automated workflows.
Optimize models, algorithms, and autonomous optimization systems to reach target quality of results.
Test and release AI systems integrated with existing machine learning and visualization tools.
Perform circuit optimization, SPICE correlation, and agent-driven design exploration.

What we're looking for

MS/PhD in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or equivalent experience.
4+ years of experience in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design.
Proficiency in writing code in Python and C++.
Experience with deep learning algorithms and AI agent frameworks like PyTorch, LangChain, or LangGraph.
Experience building AI systems for EDA, design automation, or circuit design workflows.
Research or project experience in circuit optimization, design-space exploration, or autonomous design systems.
Experience building agentic systems, autonomous optimization loops, or production-scale AI/ML platforms.
Strong verbal and written communication skills with the ability to deliver technical presentations.