CAE Vehicle Optimization and Machine Learning Engineer

Job Description At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Role GM’s Vehicle Optimization and Machine Learning team in the CAE organization delivers innovative solutions to some of the most complex performance challenges in vehicle development. The team sits at the intersection of optimization, multi‑physics CAE, and AI/ML, partnering with teams across GM to deliver enhanced vehicle performance while minimizing cost/mass and enabling faster program execution. As an Optimization & Machine Learning Engineer, you will lead the development and deployment of advanced optimization and ML methods, rapidly root cause and resolve cross‑domain performance issues, and scale optimization/physics-based AI capabilities across GM. You will be a key technical leader in turning cutting‑edge methods into robust and reusable workflows that engineers can rely on and drive value to GM products that customers love. Join us if you are passionate about innovation and energized by using advanced methods and solutions to make vehicle development faster, smarter, and enable better quality vehicles for GM customers! What You'll Do Lead the development of advanced optimization methods and workflows using both commercial CAE/optimization software and internally developed tools, leveraging state‑of‑the‑art multi‑disciplinary, stochastic/robust, and ML‑enabled technologies. Apply optimization and ML to rapidly root cause and resolve cross‑domain program performance issues across structure, crash, NVH, aero/thermal, and propulsion & energy systems while minimizing mass and cost (e.g., linear/nonlinear topology optimization; shape optimization; weld/adhesive and gauge optimization; lightweight material optimization; MDO; stochastic/robust optimization). Develop and scale ML applications for CAE by creating custom tools and workflows, and by rigorously evaluating, benchmarking, and scaling physics‑based AI solutions into CAE workflows with the goal of accelerating convergence to performance targets, root cause analysis, design-space exploration, and multi-attribute tradeoffs. Democratize optimization and ML capabilities across CAE teams by identifying high‑value adoption opportunities, providing targeted training on tools and methods, and coaching teams through real program applications. Define, maintain, and continuously improve standard work for optimization and ML (methods, templates, example studies, training material), ensuring practices are robust, reusable, and aligned with GM processes and governance. Collaborate with data, IT, and tool ownership teams to ensure optimization and ML workflows are well integrated with GM’s CAE, data management, and compute infrastructure. Your Skills & Abilities (Required Qualifications): B.S. in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Electrical Engineering, Physics, or a related discipline. 5 years experience in CAE toolsets, optimization, and ML fundamentals, which may include both industry and/or advanced research experience. Experience in one or more CAE tools such as: HyperMesh, OptiStruct, Abaqus, LS-DYNA, Simpack, Star-CCM or similar. Experience in one or more optimization tools such as: Genesis, OptiStruct, HEEDS/iSIGHT or similar. Experience with process automation and scripting, such as Python, MATLAB and VBA or similar. Knowledge of machine learning methodologies and tools, with a focus on physics‑based AI and practical experience evaluating and integrating both commercial platforms and custom‑built solutions into CAE and optimization workflows. High level of interpersonal skills to work collaboratively and effectively with others. High ability to multi‑task on different programs and projects. What Can Give You a Competitive Edge (Preferred Qualifications): M.S. in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Electrical Engineering, Physics, or Data Science (or related discipline). Ph.D. in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Electrical Engineering, Physics, or Data Science (or related discipline). Experience in CAE morphing and parametric tools such as: DEP/MeshWorks, ANSA and HyperMorph or similar. GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc). This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}. This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate. About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. 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