Sr. Propulsion Durability Attribute Engineer

Rivian Rivian · Auto · Irvine, CA · Attributes

This role focuses on developing and validating data-driven and physics-based models to predict vehicle usage, propulsion system loads, and component stress to ensure vehicle durability. It involves defining duty cycles, developing drive cycles, simulating loads, correlating simulation with real-world data, performing design of experiments, and communicating findings. While machine learning techniques are mentioned as a plus, the core of the role is in traditional engineering modeling and simulation for durability.

What you'd actually do

  1. Define the propulsion durability duty cycle for various vehicle programs, i.e., nominal and exceptional vehicle use cases, frequency, and sequence that determine propulsion system fatigue over life.
  2. Develop drive cycles for different types of vehicle use cases in different markets using real-world and vehicle-testing data.
  3. Model and simulate propulsion system loads for different drive cycles and vehicle configurations.
  4. Employ data science methods to build models of customer usage over life, i.e., develop customer behavior digital twin.
  5. Correlate simulation results with real-world fleet data and physical test results to continuously improve model fidelity.

Skills

Required

  • Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, or a related field
  • Strong understanding of the physics of vehicle propulsion systems and other energy systems
  • 4+ years experience in the automotive industry in a powertrain, propulsion, battery, or energy systems role, and/or equivalent research experience
  • Experience with vehicle powertrain systems modeling
  • Strong experience in Python (Pandas, Numpy), PySpark, Matlab, SQL
  • Experience with time series analysis
  • Demonstrated experience in analytical writing (reports, white papers, publications) and clear-and-concise presentations, with attention to detail

Nice to have

  • Working knowledge of IPG CarMaker is a plus
  • Some experience with machine learning techniques to develop classification models of behavior data (travel, driving, charging etc.) is a plus
  • Project or research experience with large time series datasets is a plus
  • Experience in fatigue life estimation (e.g., Rainflow counting, Miner’s Rule) and statistical methods (e.g., Weibull analysis)