EDA Simulation Automation Engineer

Role Summary

We are looking for a hands-on EDA Simulation Automation Engineer to join our AI-native product development team at onsemi. The role sits at the intersection of power semiconductor module simulation and engineering automation: you bring deep, practical experience running electrical, thermal, and mechanical simulations across industry-standard EDA tools, and you partner with our software and AI agent engineering team to codify that expertise into repeatable, automatable workflows. Today, every design cycle waits on a few subject-matter experts to configure tools, run simulations, and extract KPIs by hand. Your job is to break that bottleneck by defining standardized inputs, outputs, and KPI extraction logic that our platform can orchestrate and scale across engineers, teams, and products. You will also identify where commercial EDA tool licensing or concurrency limits constrain simulation throughput, and research, evaluate, or build custom and open-source alternatives that can be integrated into the platform's automation pipeline.

Success Expectations

You would become the platform's trusted domain authority for simulation configuration, execution, and KPI extraction — defining clear, documented automation contracts for each supported EDA tool, validating that automated and agent-driven simulation runs match expert-derived results, and steadily expanding the set of tools and KPIs the platform can orchestrate without manual SME intervention on every cycle.

#LI-VS1

- 3 to 5 years of hands-on experience running simulations for power semiconductor or power module design, with direct, practical use of at least two or three of: Ansys Discovery, Q3D, HFSS, IcePak, Mechanical, Cadence Virtuoso, Spectre, Calibre, SIMetrix/SIMPLIS, or PLECS.

- Solid understanding of power module electrical, thermal, and mechanical characterization and the KPIs engineers derive from each (parasitics, loop inductance, resistance, thermal resistance, junction temperature, lifetime/reliability metrics).

- Practical experience configuring simulation inputs (parameters, test benches, boundary conditions) and interpreting/post-processing raw simulation outputs into engineering KPIs.

- Scripting ability in Python sufficient to automate tool configuration, batch execution, and output parsing.

- Working knowledge of GDS/layout-based design flows and how layout, parasitic extraction, and model generation feed downstream simulation.

- Ability to translate tribal SME knowledge into clear, documented, repeatable procedures that a software or AI agent engineer can implement without deep tool expertise.

- Comfort researching and evaluating open-source, custom-built, or alternative simulation/extraction methods to work around commercial tool licensing or concurrency bottlenecks.

- Strong attention to detail in validating automated results against known-good manual runs, with a rigorous approach to signing off on release-quality simulation data.

- Good collaboration and communication skills to work closely with software engineers, SDETs, and AI coding agents on a shared automation roadmap.

#LI-VS1