Motorsports Vehicle Dynamics Optimization Engineer

Description

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Concord, NC three times per week, at minimum.

The Role

GM Motorsports is seeking a highly skilled Vehicle Dynamics Optimization Engineer to join our team. The ideal candidate will have a basic understanding of vehicle dynamics modeling and their use in numerical optimization.

What You'll Do

• Develop and maintain accurate and efficient optimization models and frameworks for use in minimum lap time formulations.
• Collaborate with engineers and designers to define simulation requirements and validate model results.
• Play a key role in the development of GM Motorsports optimization toolchain.

• Bachelor's degree in Mechanical Engineering, Physics, or related field
• Two or more years of post-graduate industry experience; candidates with a Master's or PhD and relevant experience will also be considered.
• Experience in vehicle dynamics simulation or optimal control gained through university-level FSAE, the automotive industry, or academic research.
• Basic understanding of vehicle dynamics including suspension kinematics and tire modeling.
• In-depth understanding of nonlinear optimization and their use in numerical optimal control such as direct collocation, multiple shooting, pseudo spectral methods etc.
• Understanding of numerical integration and optimization methods.
• Prior experience in formulating minimum lap time or Model Predictive Control (MPC) problems.
• Ability to program in Python, MATLAB, Julia or C++.
• Strong analytical skills with the ability to develop methods that provide actionable insights for vehicle performance improvement.
• Effective communication skills and the ability to interact well with others.
• Demonstrated ability to work effectively in a fast-paced, team-oriented environment.
• Flexibility to adapt to changing priorities and work schedules as required by motorsport demands.

• Master's or PhD in Mechanical Engineering, Physics, or related field.
• Prior experience with NLP solvers (IPOPT, SNOPT, KNITRO etc.) and symbolic frameworks (CasADi etc.).
• Familiarity with software development process - Git, code review, Continuous Integration (CI) / Continuous Deployment (CD) pipelines.
• Knowledge of motorsport simulation tools and their practical applications in high-performance racing environments.
• Demonstrated passion for creating highly accurate, numerically efficient models for motorsports.