Drimva

Rivian and Volkswagen Group Technologies

Sr. Electronics Design and Characterization Engineer (Zonal BMS)

$122K–$182K

Irvine, California · Hybrid · Senior · Full-time · Posted Sep 11, 2026

Experience
7+ years of experience in electronics design, characterization and validation testing.
Education
BS/MS or equivalent work experience in Electrical Engineering or related field.

About the job

Designs and reviews ECU electronics, then develops and executes bench-level characterization and validation for vehicle electronics, especially Zonal ECUs.

What you'll do

  • Develop verification plans and requirements, execute characterization tests, and report on test coverage and results.
  • Characterize circuit behavior, protection and diagnostics, and electronic fuses at IC and PCBA levels; provide early design and testability feedback.
  • Automate testing and analyze characterization data using Python and AI tools; collaborate across design, firmware, and test teams to resolve findings.

What they ask for

  • BS/MS or equivalent work experience in Electrical Engineering or a related field.
  • 7+ years of experience in electronics design, characterization and validation testing.
  • Experience using lab instrumentation and strong understanding of Electrical Engineering fundamentals and circuit analysis.
  • Experience with Python scripting and data analysis, and demonstrated use of AI tools for test-code development and characterization-data analysis, with human verification judgment.

Nice to have

  • Hands-on characterization of zonal/body controllers, power distribution or e-fuse modules, or motor/actuator drivers.
  • Familiarity with low-level firmware and C programming, Altium Designer, vehicle communication buses, automated test setups, or electronics simulation applications.

Pay

$121.5K – $182.3K USD base salary

Tools

Python, Claude, Claude Code, Copilot, Cursor, Altium Designer, LTspice, PSpice

AI focus

Uses AI coding agents to develop and refactor test code, and AI tooling to analyze characterization datasets for drift, variation, outliers, and failure clusters; verifies AI-generated work and conclusions.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)