Agentic AI Researcher; Hybrid

Date Posted:  Country:
United States of America

Location:

US-CT-EAST HARTFORD-RTRC K ~ 411 Silver Ln ~ RTRC K Position Role Type:
Hybrid U.S. Citizen, U.S. Person, or Immigration Status Requirements:
This job requires a U.S. Person. A U.S. Person is a lawful permanent resident as defined in 8 U.S.C. 1101(a)(20) or who is a protected individual as defined by 8 U.S.C. 1324b(a)(3). U.S. citizens, U.S. nationals, U.S. permanent residents, or individuals granted refugee or asylee status in the U.S. are considered U.S. persons. For a complete definition of “U.S. Person” go here: https://(Use the "Apply for this Job" box below).

Security Clearance Type:
None/Not Required Security Clearance Status:
Not Required

At RTX, the world's 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.

Join us and help shape the future of aerospace and defense.

The following position is to join our RTX Research Center team:

Seeking a motivated and curious candidate for an Agentic AI Researcher position in the Advanced Learning and Analytics team, part of the AI Discipline. The Advanced Learning and Analytics team researches and develops machine learning, computer vision, reinforcement learning, LLM applications and human computer interaction solutions for a variety of high impact real world problems in the aerospace, manufacturing and defense industries.

Examples include autonomous flight, material discovery and design, automated visual inspection of parts, robotic perception and prognostics and health management. We conduct basic and applied research in a stimulating multi-disciplinary environment where scientists, engineers, practitioners and subject matter experts collaborate and exchange experience. This role focuses on creating grounded, explainable and verifiable AI systems capable of operating in complex high-stakes environments such as autonomous systems, command and control, decision support and safety critical domains.

• Design and build the next-generation agentic AI systems that combine the strengths of machine learning (LLMs, RL, deep learning) with symbolic reasoning, knowledge graphs and formal methods.
• Research, design and implement novel ML approaches for multi-modal data.
• Develop algorithms, publish and present your findings to both internal and external stakeholders. Initiate, lead, and develop capabilities by seeking funding opportunities through internal and external R&D.

• You will learn to collaborate and participate in a world class multi-disciplinary research environment working.
• Learn about challenges and help develop AI solutions in critical domains of aerospace and defense.

• A University Degree In Computer Science or equivalent experience and minimum 5 years prior relevant experience, or An Advanced Degree in a related field and minimum 3 years experience Minimum 3 years of hands‑on experience in various ML techniques, off-the-shelf packages and development environments.
• Experience with building ML, LLMs and agentic systems and has a deep understanding of various ML and agentic frameworks like Pytorch, Lang Graph, Auto Gen Ability to understand and use details of an engineering problem statement, formulate it as an ML problem and identify candidate ML approaches.
• Research experience in synthesizing and combining multiple ML approaches to address novel engineering problems.

• Ph.D. in Computer Science or a related field
• 5+ years of professional ML experience with 2+ developing agentic solutions
• 5+ years of experience applying and adapting ML approaches from academic literature to real world problems.
• Experience with fine tuning LLMs for pushing the reasoning capabilities
• Prior experience in Aerospace and…