CAD Engineer

Saige Partners•San Jose, CA

23h•Onsite

About The Position

We are seeking a highly motivated CAD/EDA Automation Engineer to support and enhance ASIC design flows across the full development lifecycle (RTL to GDSII). This role focuses on building scalable automation solutions, improving design productivity, and enabling data-driven insights through advanced tooling, including AI-assisted development. This is a W2 contract position and is not eligible for C2C or W2 referral candidates.

Requirements

5+ years of experience in CAD/EDA development or related engineering roles
Strong programming skills in Python (required)
Proficiency in scripting languages such as TCL, Bash, or Perl
Basic understanding of ASIC design flow, including RTL, synthesis, place & route, STA, and verification concepts
Experience working in Linux/Unix environments and with job scheduling systems
Hands-on experience building automation frameworks, validation checks, or internal engineering tools
Familiarity with data visualization tools (e.g., Python-based dashboards, web frameworks, or BI tools)
Strong analytical and problem-solving skills with the ability to thrive in a fast-paced environment

Nice To Haves

Experience with EDA toolchains such as Synopsys, Cadence, or Siemens/Mentor Graphics
Exposure to design data formats including logs, reports, and timing/power data
Experience using AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or internal LLMs) and prompt engineering techniques
Knowledge of data engineering concepts such as data pipelines, large dataset parsing, and analytics
Familiarity with cloud or distributed computing environments
Experience developing web-based dashboards using frameworks such as Flask, Django, or React

Responsibilities

Develop, maintain, and enhance EDA/CAD flows supporting the ASIC design lifecycle (RTL GDSII)
Build and implement automated flow checks for design validation, regression monitoring, and quality metrics
Create interactive dashboards and reporting tools to track design progress, Quality of Results (QoR), and key performance indicators (KPIs)
Leverage AI/LLM-based tools to accelerate script development, debug flows, and generate insights from design data
Collaborate with cross-functional teams (RTL, Physical Design, Design Verification, DFT, STA) to identify gaps and implement automation solutions
Integrate tools and workflows across Linux-based EDA environments and compute clusters
Analyze large datasets from design runs and provide actionable insights to engineering teams
Improve engineering productivity by automating repetitive tasks and optimizing workflows
Ensure robustness, scalability, and maintainability of all developed solutions