AI Machine Learning Data Analytics Principal Engineer - P4 (Remote)

RTX is the world's largest aerospace and defense company, dedicated to solving complex problems. They are seeking a Principal Engineer specializing in AI Machine Learning and Data Analytics to develop innovative machine learning frameworks and collaborate with teams to enhance AI initiatives.

Responsibilities:

• Work with the AI/ML team to develop and apply novel machine learning frameworks and methodologies supporting the design and evaluation of turbine components for the Hot Section Engineering organization
• Deploy AI/ML applications and/or agentic workflows within Hot Section processes and tools
• Communicate technical solutions and insights to internal/external customers and organization leadership
• Advocate for and participate in fundamental AI/ML research via with our academic and external research institute partners
• Collaborate with RTX AI/ML teams to strengthen the corporate level AI/ML initiatives and infrastructure

Requirements:

• Bachelor's degree in Science, Technology, Engineering or Mathematics (STEM)
• 8 years of engineering experience, or an Advanced degree in a related field and 5 years of related engineering experience
• Master's degree or PhD preferred in Engineering, Computer Science, Physics, Mathematics, or related technical field or equivalent experience
• Familiarity with high-performance distributed computing Knowledge of state-of-the-art architectures, machine learning techniques, and algorithms with experience developing or using major deep learning frameworks (e.g., PyTorch, Tensorflow, JAX)
• Experience with solving and using machine learning for real world problems involving scientific & engineering simulations (e.g., structural, CFD, thermal)
• Strong Python programming skills and SQL proficiency
• Publication record in the field of scientific machine learning / AI4Science
• Experience with gas turbine engines or airbreathing propulsion applications
• Demonstrated understanding and of uncertainty quantification (UQ) and statistical inference techniques