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 platforms automation pipeline.

Success Expectations

You would become the platforms 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

• Own hands-on simulation execution for power module characterization across electrical thermal and mechanical domains using tools such as Ansys Discovery Q3D HFSS IcePak Mechanical OptiSLang Cadence Virtuoso Spectre Calibre (DRC/LVS/PEX) SIMetrix/SIMPLIS and PLECS.
• Define and validate KPI extraction logic from raw simulation outputs including loop inductance on-resistance (RDS(on)) switching energy junction temperature (Tjmax) thermal resistance (Rth) and power-cycling lifetime.
• Design standardized parameterized test-bench definitions and post-processing rules that convert raw tool outputs into compliance-ready KPI values.
• Document tool-specific configuration input/output file schemas and scripting hooks (Python tool APIs batch/command-line modes) so engineering can automate configuration dispatch and result ingestion for each tool.
• Partner with software and SDET engineers to build and validate simulation regression pipelines ensuring automated or agent-driven runs reproduce trusted manually-derived golden results.
• Capture and formalize simulation best practices known-good configurations and troubleshooting knowledge that today lives only with individual SMEs so it can be reused across teams and products.
• Support AI agent tool-calling integrations for EDA tools by defining safe well-scoped automation boundaries required parameters and expected outputs for each tool.
• Research evaluate and prototype custom or open-source alternatives to commercial EDA tools where per-seat licensing or concurrency limits throttle agentic parallelized simulation throughput and propose where such alternatives can safely substitute or supplement licensed tools.
• Review and sign off on automated simulation results and generated KPI/compliance reports against domain expectations before release certification.
• Collaborate with model-generation and layout/GDS teams to ensure electrical and thermal models feeding simulations remain accurate and traceable.
• Provide simulation domain expertise during project scoping test-suite definition and requirements-to-KPI mapping discussions.

#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