Lead Engineer - Probabilistic Design - Aerospace Research Overview Lead Engineer - Probabilistic Design - Aerospace Research role at Jobs via Dice. The successful candidate will contribute to the development of state-of-the-art probabilistic methods, engineering design tools, and solving challenging real-world industry problems in metamodeling/surrogates, machine learning, model calibration and validation, uncertainty quantification, optimization and robust design, inverse modeling, and engineering analysis model validation for GE Aerospace and U.S. government projects. As a member of the probabilistic design team, you will contribute to the development of methods and tools to deliver world-class solutions for new product introduction (NPI), services, energy transition, and the future of flight throughout the GE Aerospace company, including collaboration with U.S. government partners. Responsibilities Collaborate with GE Aerospace design and services communities in the development of methods for probabilistic design, machine learning and optimization Apply probabilistic design, machine learning and optimization methods to real-world industrial applications for NPI design and services maintenance planning for GE Aerospace business Implement probabilistic design, machine learning and optimization methods into GE internal design and services tools Train and coach GE engineers on probabilistic and machine learning methods and tools Lead and manage projects, people and funding Qualifications / Requirements Doctorate degree in Mechanical Engineering, Aerospace Engineering with at least 3 years industrial experience, or related discipline OR Master’s degree in Mechanical Engineering, Aerospace Engineering, or related discipline with at least 8 years industrial experience Experience in probabilistic design, machine learning, and/or optimization of engineering components and systems Fundamental knowledge in probabilistic methods, machine learning, Bayesian methods, and optimization applied to engineering design problems Experience with leading government programs and proposal writing Fundamental understanding of solid mechanics and tools used in structural analysis such as ANSYS or similar FE software Ability to develop, modify and utilize custom computer codes in Python, C++, Matlab, Visual Basic, Perl, R, etc Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening Must be willing to work onsite in Niskayuna, NY Desired Characteristics In-depth understanding and methods development experience in dynamic Bayesian networks, Bayesian networks, physics-based/physics-informed forecasting, time-series modeling, image-based surrogates, probabilistic deep learning, transfer learning, physics discovery, uncertainty quantification, model calibration, verification & validation, DOE/DACE, metamodeling, sensitivity analysis, and inverse design Experience in solving complex engineering problems using probabilistic and machine learning methods above Experience with mechanical design and analysis methods Experience with software development Experience with fracture mechanics Demonstrated interpersonal, leadership and communication skills in a global team environment Strong interpersonal skills and analytical skills Ability to work across all functions/levels as part of a team Ability to work under pressure and meet deadlines Excellent written and verbal communication skills Compensation & Benefits The base pay range for this position is 90,000 - 175,000 USD annually. The specific pay offered may be influenced by a variety of factors. This posting is expected to close on January 2, 2026. Benefits include healthcare (medical, dental, vision, prescription), retirement plans, education and family support, paid time off, and more. GE Aerospace is an Equal Opportunity Employer. Relocation assistance provided: Yes.

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