Professional, Engineering‑Multidisciplinary Design, Analysis & Optimization (MDAO / MDO) Location: Dorval, QC, CANShift: Day jobStatus: Regular Responsibilities Design, develop, and maintain software tools dedicated to a large-scale industrial‑level multidisciplinary design, analysis, and optimization (MDO/MDAO) framework. Collaborate closely with aircraft design, analysis, systems, and performance teams to ensure alignment between developed tools and business needs. Assume a technical leadership role in the maintenance, evolution, and continuous improvement of an existing multi‑fidelity optimizer. Design, implement, and validate new algorithmic approaches within internal codebases, with a particular focus on advanced Bayesian and multi‑fidelity optimization strategies. Implement optimization under uncertainty (OUU) strategies to support decision‑making in the presence of epistemic and aleatory uncertainties, and assess their impact on aircraft performance, design margins, and technical risks. Perform advanced statistical analyses, including sensitivity analyses and uncertainty propagation. Develop and train machine learning models to support optimization, performance prediction, and uncertainty quantification. Integrate machine learning models, optimization algorithms, and statistical methods within the company’s Digital Engineering environment. Present results, methodological choices, and technical orientations to senior experts, fellows, and decision‑making bodies. Conduct active technological and scientific reviews in MDO, Bayesian optimization, uncertainty quantification, and optimization under uncertainty to influence the technical roadmap. Supervise interns. Qualifications Master’s degree or Ph.D. in Aerospace Engineering, Mechanical Engineering, Applied Mathematics, Scientific Computing, or a related field. Specialization in Multidisciplinary Design, Analysis, and Optimization (MDO/MDAO), including usage of MDO frameworks such as openMDAO, GEMSEO, etc. Strong expertise in multi‑fidelity optimization methods and an excellent understanding of Bayesian methods, including Bayesian optimization and probabilistic models. Ability to understand, analyze, and synthesize scientific publications to translate state‑of‑the‑art methods into algorithmic solutions applicable in an industrial context. Comfortable with software development, including version control using Git and CI/CD practices. Experience with software development assisted by AI agents is considered an asset. Experience with High‑Performance Computing (HPC) and cross‑platform (Windows, Linux) computing. Experience or strong interest in integrating advanced tools and software within a Digital Engineering environment. Knowledge or experience with uncertainty quantification (UQ) and optimization under uncertainty (OUU) approaches (asset). Strong understanding of machine learning concepts, including supervised and unsupervised approaches, principles of training, validation, hyperparameter tuning, and model evaluation (asset). Good understanding of aircraft systems, their interactions, and their impact on aircraft configuration and structure. Open‑minded, curious, rigorous, and methodical. Valuing knowledge sharing and seeking a harmonious team environment. Excellent analytical and problem‑solving skills. Strong communication skills with the ability to adapt communication to the audience. Benefits Insurance plans (Dental, medical, life insurance, disability, and more) Competitive base salary Retirement savings plan Employee Assistance Program Tele Health Program Equal Opportunity Employer Bombardier is an equal opportunity employer and encourages persons of any race, religion, ethnicity, gender identity, sexual orientation, age, immigration status, disability or other applicable legally protected characteristics to apply. #J-18808-Ljbffr