POSITION SUMMARY: This position extends traditional aerodynamic product development by creating and integrating custom AI/ML applications, pipelines, and data systems into turbomachinery workflows. The engineer supports aero stage development, improves predictive capability using data-driven methods, and develops scalable tools to enhance engineering efficiency, bridging aero performance engineering with modern software and data engineering.
The role also includes aggregation and structuring of legacy TAG data to generate insights on global Category 1 & 2 part reliability, as well as aggregation and restructuring of legacy sales and operational datasets to enable machine learning model development and training.

KEY ACCOUNTABILITIES:

• Design, train, and deploy custom AI/ML models (e.g., neural networks, CNNs, surrogate models) to enhance compressor/turbine performance prediction and accelerate simulation workflows.
• Support aerodynamic stage development through data-driven analysis, performance correlation, optimization, and physics-informed / hybrid ML methods.
• Build and maintain structured data systems for geometry, CFD, test, and engine datasets using scalable ETL pipelines, SQL, and cloud-based data architectures.
• Integrate AI/ML models into engineering workflows including CFD, FEA, test analysis, and product development toolchains.
• Develop and own end-to-end ML pipelines (Python, APIs, dashboards), including feature engineering, model training, hyperparameter tuning, validation, and deployment.
• Collaborate with aero engineers to augment classical turbomachinery and applied physics methods with data-driven and statistical learning approaches.
• Execute rigorous model validation against test stand and engine data, including uncertainty quantification, robustness assessment, and performance generalization.
• Develop reusable ML frameworks and guide technical execution, documentation, and adoption of AI/ML methods across Product Development initiatives.

QUALIFICATIONS:

• Bachelor of Science in Computer Science, Statistics, Engineering, or related discipline with strong foundation in applied mathematics and statistics.
• Strong programming skills in Python; experience with additional languages (JavaScript, C/C++) and software engineering practices is a plus.
• Hands-on experience developing, training, and optimizing ML models (PyTorch, TensorFlow), including neural networks (NNs, CNNs) and data-driven modeling approaches.
• Experience with SQL and database systems; familiarity with ETL pipelines, Azure Data Factory, or similar distributed data platforms.
• Solid understanding of algorithms, numerical optimization, probability theory, applied statistics, and data structures.
• Demonstrated experience developing production-quality data-driven applications, ML models, or analytical tools.
• Exposure to aerodynamics, turbomachinery, CFD, or physics-based engineering systems preferred.
• Strong analytical and problem-solving capability with ability to translate complex engineering problems into scalable ML/statistical solutions.

Salary Range:

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