Aeromechanics Machine Learning Engineer

Job Description: 3D ML/AI and MDO Methods Engineer (Gas Turbines) We are looking for a highly skilled Research and Development Engineer to join our team. This role focuses on developing and deploying 3D machine learning applications, surrogate models, and digital-twin-enabled workflows for the turbomachinery multi-disciplinary optimization (MDO) process chain. Develop 3D ML-based design tools and software applications for aerodynamics, heat transfer, mechanical integrity, and lifing. Develop and validate surrogate models / digital twins according to user and project needs. Build automation scripts and workflows for MDO data preparation, model training, validation, and deployment. Integrate ML models into optimization loops and decision-support workflows. Automate engineering process steps where appropriate. The ideal candidate should have knowledge of AI / ML methods and turbomachinery engineering fundamentals. Key responsibilities include requirements gathering, development

documentation, deployment, and maintenance of software applications. The engineer will also prepare training material and presentations to support adoption by engineering teams and improve project execution in terms of time, cost, and quality. Basic Qualifications: Bachelor’s degree in mechanical engineering, aerospace engineering, computer science, applied mathematics, or related field. Preference will be given to higher education like Masters or PhD. Strong background in developing engineering design tools, scripts, or software applications. Proficient programming experience (Python, C++, Java) and good Linux skills. Highly motivated, proactive, and self-directed. Excellent interpersonal and communication skills. Must be eligible to work on control technology as required by U.S. export control regulations. Technical Expertise: Strong understanding of ML model development, validation, and deployment. Proficiency in scientific and ML software stack (for example NumPy, pandas, scikit-learn, PyTorch or TensorFlow). Working knowledge of Gitlab/Git and Linux. Knowledge of uncertainty quantification and model-governance best practices is a plus. Professional Experience: 3+ years of experience (or relevant PhD experience) in gas or aero turbine design, MDO workflows, or ML-enabled engineering applications. Preferred Qualifications: Good understanding of gas turbine fundamentals for component design and engineering methods. Strong statistics, analytics, and mathematics skills. Knowledge of databases and data structures. Experience with LLMs, AI, ML, deep learning, surrogate models, or digital twins. Experience with high-performance computing (HPC) environments. Experience in Azure, AWS, or hybrid cloud environments. Experience developing APIs and internal engineering services.