About the job
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. 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.
Responsibilities
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.
Qualifications
Minimum
B.S, M.S in Computer Science or a related field
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, LangGraph, AutoGen
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.
Must be authorized to work in the U.S. without sponsorship now or in the future. RTX will not offer sponsorship for this position.
Preferred
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 finetuning LLMs for pushing the reasoning capabilities
Prior experience in Aerospace and