Senior Applied Machine Learning Engineer - VLSI Design

Nvidia

Hybrid

Quick summary

Work type
Hybrid
Location
Santa Clara, CA
Salary
$152,000–$230,000 / yr
Posted
3 days ago

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $212k
This role $191k
$139k most similar roles pay here $271k

This role pays less than 69% 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 239 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 563 open roles on FindRole.

Listed pay typically runs $168,000–$264,500 across 556 roles with salary data.

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At a glance

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

Join our cutting-edge team as a Senior AI Engineer specializing in circuit design automation, where you will collaborate on projects involving pre-silicon and post-silicon hardware data, circuit optimization, SPICE correlation, and AI systems for EDA. Your daily tasks include translating requirements into data science problems, architecting solutions, testing models, and integrating them with existing tools like PyTorch and LangChain. You’ll analyze datasets, build self-improving workflows, and optimize algorithms to enhance quality of results. Ideal candidates hold an MS/PhD in Electrical/Computer Engineering or related fields, with 4+ years of experience in circuit design, VLSI, EDA, and AI-driven systems. Proficiency in Python and C++, along with a track record of building agentic systems for autonomous optimization, is essential.

What you'll do

  • Translate complex requirements into AI/ML problems and build corresponding solutions.
  • Architect and develop AI systems that integrate with existing EDA tools and workflows.
  • Analyze large datasets to extract features and build predictive models for circuit design.
  • Optimize machine learning models and algorithms to enhance quality of results (QOR).
  • Develop autonomous optimization loops and self-improving AI systems for circuit design.

What we're looking for

  • MS/PhD in Electrical/Computer Engineering, Computer Science, or equivalent experience with 4+ years in circuit design, VLSI, EDA.
  • Proven expertise in Python and C++ programming, applied math, ML, and software development.
  • Experience with deep learning algorithms, AI agent frameworks, and tools like PyTorch, LangChain/LangGraph.
  • Background in building AI systems for EDA, design automation, or circuit design workflows.
  • Research experience in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems.
  • Ability to build agentic systems, autonomous optimization loops, self-improving AI systems, and production-scale platforms.

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