Senior Aeromechanics Machine Learning Engineer

Siemens Energy; Greater Lincolnshire; On-site; GBP 70,000 - 110,000; Full time

Job summary

Siemens Energy Limited in Lincolnshire, UK, is seeking a Senior Aeromechanics Machine Learning Engineer to develop 3D ML-based design tools and digital twins for turbomachinery MDO processes. You will build surrogate models and automated workflows, integrating ML into optimization loops to speed up engineering tasks.

The role requires a strong background in ML methods, turbomachinery knowledge, and proficiency in Python, C++, Java, and Linux, with experience in HPC and cloud environments.

Qualifications

• Bachelor's degree in mechanical or aerospace engineering or related field.
• 3+ years in gas or aero turbine design, MDO workflows, or ML-enabled engineering.
• Proficient in Python, C++, Java and Linux.
• Experience with ML tools and digital twins is a plus.

Responsibilities

• Develop and deploy 3D ML-based tools and surrogate models for turbomachinery.
• Create and validate digital-twin-enabled workflows and MDO processes.
• Build automation scripts for data prep, training, validation, and deployment.
• Integrate ML models into optimization loops and decision-support.
• Prepare training materials and support adoption by engineering teams.

Skills

3D ML/AI

Python

C++

Java

Linux

ML model development

Computational design tools

Gitlab/Git

HPC environments

LLMs / AI / ML / digital twins

Education

Bachelor's degree in mechanical/aerospace engineering, computer science, or applied mathematics

Masters or PhD preferred

Tools

NumPy

pandas

scikit-learn

PyTorch

TensorFlow

Job description

Senior Aeromechanics Machine Learning Engineer

About the Role

Job ID 303169 Location United Kingdom of Great Britain and Northern Ireland Lincolnshire Lincoln Company Siemens Energy Limited Organization Gas Services Business Unit Distributed Full / Part time Full-time Experience Level Professional / Experienced

Senior Engineer - Aero-mechanics

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 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.