Mechanical, Software and Lifing Lead

As a Mechanical Software and Lifing Lead in the Condition Based Engineering team of Solar Turbines, you will solve real-world engineering problems and pioneer the development of engine prediction capabilities for our fleet of gas turbines. Our digital twins combine engine data, reduced‑order physics models, and machine learning to predict and optimize the life of critical gas turbine components. When you join Caterpillar, you are part of a global team that cares about each other and builds stronger, more sustainable communities.

Responsibilities

• Develop and maintain methods and tools for turbomachinery components and supporting systems, learning and applying concepts of lifing, probabilistic analysis, coding architecture, and machine learning.
• Simulation: Develop models and tools to simulate gas turbine hardware such as blades, disks, and nozzles, accounting for key degradation mechanisms, and model adjacent systems such as cost, sales, and manufacturing operations.
• Software development: Develop and maintain backend software that supports the digital twins and their wider application internally for analysts and externally for customers.
• Leadership: Lead projects and small teams to deliver engineering tools and models, and interface with other development teams on multiteam software projects.
• Initiative: Propose, promote, and act on your own initiatives and ideas to drive progress and innovation.
• Engagement: Be highly engaged in collaborative problem solving, applying your background to push quick resolutions and long‑term innovation, find opportunities for improvement and implement solutions.
• Team participation: Actively utilize your engineering and analytical skills as a member of an international, multidisciplinary team.

Qualifications

• Educational Background: Degree in Mechanical Engineering or a related subject (e.g., Physics, Aerospace Engineering, Materials, Mechatronics) and 7+ years experience applying knowledge to solve real‑world problems.
• Coding Experience: Experience using C#, C++, or Python to process large datasets and automate complex engineering tasks to improve accuracy, efficiency, and quality of models.
• Component Lifing Experience: Understanding of lifing approaches used for mechanical equipment and systems in safety‑critical industries such as power, aerospace, or nuclear that account for fatigue, creep, and other degradation mechanisms.
• Effective Communications: Good verbal and written communication skills with the ability to interface with engineers from various disciplines and backgrounds.
• Manage Multiple Priorities: Ability to manage multiple concurrent projects, making effective judgments on time allocation, priorities, and required quality to ensure projects are completed efficiently and thoroughly.
• Problem Solving: Ability to solve problems and innovate pragmatic solutions when the right path is unclear.
• Technical Excellence in Gas Turbines: Thorough understanding of gas turbine fundamentals, component degradation physics, and how to account for these in lifing calculations across a fleet of gas turbines (may be gained from working with a gas turbine OEM or partner organization).
• Big Picture Thinking: Experience solving challenging problems pragmatically to provide value for internal and external customers.
• Educational Background: Masters or higher degree with a specialty in gas turbines.