Sr. Electromagnetic Design Engineer

Rivian•Torrance, CA

10d•Remote

About The Position

As an Electromagnetic Design, Analysis & Characterization Engineer on the Advanced Hardware team, you will bridge the gap between high-fidelity physics simulation, component development, and experimental laboratory validation. You will own the electromagnetic design, advanced analytical analysis, and physical testing of critical magnetic, electric motor lamination, and electrical insulation materials across both electric traction motors and power electronics magnetic units. Additionally, you will utilize 2D/3D finite element analysis (FEA) and rigorous mathematical modeling to develop next-generation magnetic components and automotive actuators, driving the complete data loop from hardware testing to virtual simulation for Rivian’s upcoming mass-market R2 platform and future L4 autonomous vehicle architectures. You will work closely with cross-functional teams in Thermal, Mechanical, NVH, Vehicle Integration, and Physical Validation Labs to ensure automotive-grade reliability, efficiency, and cost-down targets.

Requirements

MS or PhD in Electrical Engineering, Applied Mathematics, or Physics with a core research focus on Electromagnetics, Magnetic Materials, Electric Machines, or Analytical Electromechanical Modeling.
2-5 years of experience on traction motor/magnetic insulation testing, electromechanical component design, and numerical math modeling.
Strong mathematical and analytical foundation with proven experience deriving and implementing physical equations for electromagnetic fields, loss optimization, or material degradation kinetics.
Proficiency with electromagnetic simulation software such as Ansys Electronics Desktop (Maxwell, Icepak) or JMAG.
Expert-level scripting skills in Python or MATLAB/Simulink for custom math model development, automated data post-processing, and laboratory equipment data automation.
Direct hands-on experience operating laboratory test equipment (e.g., impedance/LCR analyzers, high-voltage insulation/hipot testers).
Highly self-driven with a strong root-cause mindset to diagnose complex test-to-simulation discrepancies, identify fundamental failure modes, and leverage excellent cross-functional communication skills to translate complex electromagnetic data into actionable hardware design choices.

Responsibilities

Design and execute laboratory testing pipelines for magnetic materials, and electric motor insulation materials.
Conduct experimental characterization of electrical steel laminations (B-H curves, core losses).
Execute accelerated thermal aging and dielectric breakdown studies on electrical machine insulation to evaluate long-term reliability and degradation mechanisms.
Develop and implement advanced analytical and numerical mathematical models to describe non-linear electromagnetic behavior (including custom core loss models, frequency-dependent copper losses, and non-linear permanent magnet demagnetization boundaries).
Establish rigorous experimental verification pipelines to validate analytical models against laboratory test data, and seamlessly integrate these verified frameworks into large-scale, high-fidelity finite element (FE) simulations to ensure simulation-to-test alignment and reduce prototyping reliance.
Perform advanced 2D and 3D transient, magnetostatic, and frequency domain electromagnetic finite element analysis (FEA) using Ansys Maxwell or JMAG for electric motor components, high-frequency magnetics, and low-frequency automotive actuators.
Optimize magnetic circuit topologies to maximize torque and power density while mitigating magnetic saturation and torque ripple.
Execute large-scale parametric optimization sweeps to balance multi-objective trade-offs between packaging constraints, structural integrity, electromagnetic efficiency, and strict automotive cost targets.
Conduct coupled simulations to evaluate electromagnetic performance under severe duty cycles.
Collaborate with cross-functional engineering teams (Thermal, Mechanical, NVH, and Vehicle Integration) to align simulation boundaries, validate model accuracy, and optimize overall motor performance.