📍 San Francisco | 🏢 5 Days Onsite

Location: Onsite in San Francisco

Compensation: Competitive Salary + Equity

EMI Simulation Engineer

Who We Are

Engineering simulation is one of the last major categories of software that AI hasn't rebuilt. The tools used to design aircraft, ships, reservoirs, and medical devices still run on numerical methods that are decades old, and an engineer can wait a full day for a single answer. UniversalAGI is building foundation models that learn physics directly from data, and they are already running in early deployments on real computational fluid dynamics and reservoir engineering problems for some of the largest industrial and defense organizations in the world.

We are a team of 25 researchers and engineers in San Francisco backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico).

About the Role

As an EMI & RCS Simulation Engineer, you'll lead UniversalAGI's foundation model development in electromagnetics. Today our models learn physics directly from data primarily for computational fluid dynamics and reservoir engineering. Electromagnetic interference and radar cross section are the next frontier, and you’ll play a central role in shaping the datasets and modeling approaches for it.

You'll run and automate industry standard EM simulations to generate the high fidelity, well labeled data our models train on, and to produce the EMI and RCS analysis that supports our customers directly. This is a hands on, execution heavy role, not a research seat: we need deep, practical fluency with real EM simulation tools and real electromagnetic problems, applied at the volume and consistency a foundation model needs to learn from.

What You'll Do

Design and run EMI and RCS simulation campaigns using industry standard EM solvers (Ansys HFSS or SIwave, Dassault CST Studio Suite, Altair Feko, Cadence Clarity, or equivalent) to generate high fidelity training data for UniversalAGI's foundation models.

Build and maintain large, well labeled, physics accurate simulation datasets spanning a wide range of EMI and RCS geometries and scenarios, ensuring the coverage and quality our models need to learn from.

Produce the simulation derived EMI and RCS analysis and deliverables that power our customer engagements, translating real world customer requirements into simulation setups and validated results.

Validate simulation outputs against measured data and known analytical solutions so the datasets feeding model training are physically trustworthy, not just fast to generate.

Automate and scale simulation pipelines, including scripting and batch job orchestration, so data generation keeps pace with what the model training pipeline needs next.

Work directly with the AI research team to translate model performance gaps into new simulation campaigns, and help define UniversalAGI's technical approach to electromagnetics as we bring it into the foundation model stack for the first time.

Qualifications

4+ years of hands on, production experience running electromagnetic simulations for EMI/EMC and/or RCS analysis, in industry or a defense/aerospace program setting.

Deep, practical fluency with at least one major commercial full wave EM solver (Ansys HFSS or SIwave, Dassault CST Studio Suite, Altair Feko, or Cadence Clarity), including simulation setup, meshing, and results validation.

Strong grounding in Maxwell's equations and electromagnetic theory as applied to real engineering problems, not just coursework.

Experience validating simulation results against measured or experimental data, with a clear sense of where simulation is and isn't trustworthy.

Working proficiency in Python (or similar) for scripting, automating simulation pipelines, and structuring simulation output into clean, well labeled datasets.

Comfort working across two audiences at once: internal model training needs and customer facing analysis, and the ability to translate clearly between them.