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.