Durability Modeling & Simulation Engineer

We are seeking a highly motivated Durability Engineer to join our Durability Analytics and Targets team. In this early-career position, you will work alongside and learn from senior team members to analyze real-world, virtual, and test data. You will develop first- principles physics, multibody dynamics, and data-science models to predict loads, vibration, and fatigue damage for various suspension, body, and propulsion components. You will help us answer a critical question: How do we design our vehicles to remain robust across diverse customer use cases over their entire lifecycle?

Apply your fundamentals in vehicle physics to:

• Assist in and develop multibody dynamics models to calculate loads on various suspension, body, and propulsion components during vehicle operation.
• Estimate fatigue damage and derive component fatigue block cycles.
• Develop comprehensive vibration and shock requirements.

Learn and employ methods in the fast-growing area of data-science to:

• Model real-world vehicle usage and establish accurate durability targets.
• Develop reduced-order models to efficiently calculate vehicle loads and fatigue damage.
• Analyze wheel-force transducer data, accelerometer data to identify root cause of failures, develop accelerated duty cycles, and perform sensitivity studies

Leverage AI and coding skills to:

• Automate processes such as component block cycle development and accelerated duty cycle development.

• Standardize Durability team’s inputs, outputs, and communication with cross-functional teams

• Bachelor’s degree in Mechanical engineering or Aerospace engineering.

• A solid grasp of vehicle physics, mechanics of materials, fatigue, and modeling/simulation.

• Practical exposure through project-based work to modeling and simulation in MATLAB or Python. Exposure to MSC Adams, Simpack, nCode, or CarMaker is a plus.

• An analytical mindset coupled with clear and effective communication skills.

• Passion for Rivian’s mission and commitment to fostering an inclusive, respectful, and collaborative workplace.

• Familiarity to time-series analysis, clustering algorithms, and deep learning is advantageous.