Senior Aeromechanics Machine Learning Engineer

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.

What You Bring

• 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.

About The Team Our Gas Services sector delivers eco-friendly power generation solutions via maintenance and decarbonisation. We deliver zero or low-emission power generation, steam turbines, and generators, all in one place. Our team is committed to upgrading and digitalising the fleet, presenting chances for decarbonisation through inventive service options.

Who is Siemens Energy? At Siemens Energy, we are more than just an energy technology company. With +100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world’s electricity generation.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo

Our Commitment to Diversity

Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion, and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energise society, all of society, and we do not discriminate based on our differences.

Rewards/Benefits

• Competitive salary and performance-based incentives.
• Comprehensive health and wellness benefits.
• Opportunities for continuous learning and career development.
• Flexible working arrangements to support work-life balance.
• A dynamic and inclusive work environment that values diversity.
• Access to brand-new technology and innovation projects.

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