Air-Vehicle Multidisciplinary Design Analysis and Optimization (MDAO) Engineer

Job description

Job responsibilities

• Lead MDAO Initiatives: Collaborate with air-vehicle design and analysis disciplines and technical leadership across AD&S programs to define and execute critical trade studies, building robust, data-driven workflows…
• Strategic Development: Work with the AD&S Engineering Tools Lead to shape the strategic roadmap for MDAO capabilities, deploy multi-fidelity workflows across programs, and document available resources.
• Geometry Generation: Design and implement robust, parameterized, watertight, CAD-based scripted geometry models to enable multidisciplinary analysis and ensure consistent results.
• Accelerate Design Insights: Drive the earlier integration of disciplinary models into the design process, providing rich data to inform critical tradespace decisions.
• Pioneer Methodologies: Develop and apply cutting-edge MDAO methods and models to significantly increase the efficiency and effectiveness of AD&S teams.
• Apply AI to MDAO: Innovate and deploy Artificial Intelligence (AI) solutions within MDAO workflows to enhance efficiency and discovery.
• Optimize Workflows: Identify gaps, bottlenecks, and recommend solutions to continuously improve multi-fidelity workflows.
• Execute Advanced Analyses: Assemble and run automated MDAO workflows, Design of Experiments (DOE), optimizations, sensitivity analyses, and Uncertainty Quantification (UQ) for AD&S programs.

Job requirements

• Bachelor's or higher degree from an accredited program in Engineering, Physics, Mathematics, or Computer Science.
• 8+ years of experience in aircraft and/or missile design and analysis.
• 3+ years of hands-on experience developing MDAO methods, demonstrating broad understanding across multiple engineering disciplines, such as: Cost, Configuration Design, Mass Properties, Lethality, Structures,…
• Proven experience in developing and deploying MDAO workflows and toolsets.
• Expertise in applying optimization, sensitivity analysis, Uncertainty Quantification (UQ), and Design of Experiments (DOE) methods.
• Demonstrable ability to apply large language models and/or develop AI models to enhance or accelerate engineering processes and tasks with measurable efficiency improvements.
• Proficiency in higher-level scripting languages (Python, MATLAB) within a version-controlled environment (preferably Git), with exposure to compiled languages (C, C++, FORTRAN).
• Strong collaborative spirit and a desire to contribute to methods development and process workflows in a team setting.