Ai/cad Software Engineer Software

Front Door Defense; Atlanta, Northern (GA, KY); Hybrid; USD 130,000 - 180,000; Full time

Job summary

Falcomm is seeking a full-time AI/CAD Software Engineer in Atlanta, GA to develop AI-driven CAD tools for RF PA design and EM simulations. You will lead ML-driven data pipelines, automate PA datasets, and integrate ML inference into RF EDA workflows to accelerate PA tape-outs.

The role combines software engineering, machine learning, and RF physics, offering collaboration with RF/PA architects and a fast-paced hardware team.

Responsibilities

• End-to-End ML Pipeline Development: Architect and maintain code for training and serving ML models.
• PA Design Automation: Automate datasets from PA load-pull and EM simulations.
• Tool Integration: Integrate ML inference into RF EDA to provide feedback to PA designers.
• Software Eng. Excellence: Implement testing, CI/CD, code reviews in an agile hardware team.
• Cross-Functional Collaboration: Work with RF/PA architects on impedance matching and linearity requirements.

Skills

Machine learning

Python

RF design

EM simulations

CI/CD

Tools

ML libraries

CI/CD pipelines

RF EDA tools

Job description

AI/CAD Software Engineer

Develop AI-driven CAD tools to automate PA design and EM simulations.

Location: Atlanta, Georgia

About The Role

AI/CAD Software Engineer

Are you passionate about applying machine learning to transform the future of RF hardware? At Falcomm, we are revolutionizing wireless communications by integrating AI-driven solutions into the design of our energy-efficient Power Amplifier (PA) products. We are seeking a full-time AI/CAD Software Engineer to join our team. You will lead the development of intelligent tools that enhance RF workflows, automate complex EM simulations, and accelerate the tape-out of high-performance PAs. This role sits at the intersection of software engineering, machine learning, and RF physics, perfect for a developer who wants to get close to the hardware.

Responsibilities:

• End-to-End ML Pipeline Development: Architect and maintain robust code for training and serving ML models that predict and optimize RF performance metrics.
• PA Design Automation: Automate the creation and management of large-scale datasets derived from PA load-pull and electromagnetic (EM) simulations.
• Tool Integration: Seamlessly integrate ML inference engines into existing RF EDA infrastructure to provide real-time feedback to PA designers.
• Software Engineering Excellence: Establish best practices including comprehensive testing, CI/CD pipelines, and code reviews within an agile hardware team.
• Cross-Functional Collaboration: Partner closely with RF/PA architects to translate complex impedance matching and linearity requirements into deployable software solutions.