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

U.S. Citizen, U.S. Person, or Immigration Status Requirements

U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Security Clearance Type

None/Not Required

Security Clearance Status

Not Required

At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.

Pratt & Whitney is a world leader in the design, manufacture and service of aircraft engines and auxiliary power systems and has been revolutionizing modern flight for over 100 years. Join us and help shape the future of aerospace and defense.

What You Will Do

• 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.

Qualifications You Must Have

• 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.

Qualifications We Prefer

• 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