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# Aeromechanics Machine Learning Engineer

- **Company:** SIEMENS ENERGY LIMITED
- **Location:** UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Flutter, Artificial Intelligence, Software Applications, Computational Fluid Dynamics, Python (Programming Language), Linux System Administration, Machine Learning, Test Data, Software Organization, Pytorch, Git, Engineering Base
- **Published:** September 30, 2026
- **Apply:** https://www.careerjet.co.uk/job/gb31cdcea7bdf8cfdc04d9f0bf45b8c448/eaa

## About the Role

* A Master's or PhD degree in mechanical engineering, aerospace engineering, or related field.
 * 3+ years of experience (or relevant PhD experience) in gas turbine components design and analysis, or ML-enabled engineering applications.
 * Strong knowledge of turbomachinery aerodynamic design and analysis, computational fluid dynamics (CFD), simulation and optimisation methods.
 * Experience in aeromechanical analysis, including flutter, forced response and non-synchronous vibrations.
 * Experience in solid mechanics, structural dynamics and finite element analysis (FEA) is beneficial.
 * Strong experience in developing engineering design tools, scripts, or software applications.

 * Strong understanding of machine learning model development, validation, and deployment.

· Hands-on experience developing machine learning solutions using Python and frameworks such as PyTorch.

 * Working knowledge of Git, Linux environments and modern software development practices.

 * Highly motivated, proactive, and self-directed.
 * Excellent interpersonal and communication skills to collaborate across diverse cultures and disciplines.

## Description

As an Aeromechanics Machine Learning Engineer, you will develop and implement AI-enabled methods for the aerodynamic and mechanical design of turbomachinery components. You will combine expertise in aerodynamics, aeromechanics and machine learning to create surrogate models that allow larger design spaces to be explored and aeromechanical risks to be assessed earlier in the design process.
As part of a global and multidisciplinary team, you will work closely with engineering specialists, data scientists and software developers. You will provide technical leadership across disciplines and ensure that new machine learning methods are physically meaningful, appropriately validated and suitable for use in engineering design.
How you'll make an impact

 * Perform multidisciplinary analysis and optimisation of gas turbine components including compressor and turbines.
 * Develop automated workflows for engineering design, analysis and multidisciplinary optimisation (MDO).
 * Develop 3D machine learning (ML) design tools and surrogate models for aerodynamic and aeromechanical applications.
 * Integrate machine learning models into engineering workflows and design optimisation processes.
 * Validate surrogate models against high-fidelity simulation and test data and define their applicability limits.