Mobius Logic, Inc. is offering a fully remote 12-week summer internship for Future AI Programmers, starting mid-May 2026 and targeting motivated first or second-year undergraduate students in Computer Science, Engineering, Mathematics, Data Science, or related fields. Interns will learn fundamental coding skills and IDE usage while focusing on one of three specialized areas: Reinforcement Learning, Robotics, or Multi-agent AI Training. The program includes completing a supervised research project under senior researchers, participating in weekly research meetings with active projects, collaborating with experienced professionals, and engaging in academic activities like reading and presenting papers with a final presentation at the internship's conclusion. Candidates must be U.S. citizens, demonstrate curiosity and eagerness to learn, and have familiarity with at least one coding language.

Mobius Logic, Inc. is offering a fully remote 12-week Computational Mathematician internship starting mid-May 2026, focused on advancing hybrid human-AI autonomous systems for applications like satellite operations and multi-agent teaming. The role involves working on cutting-edge projects including training agents using reinforcement learning, developing dynamic teaming algorithms, creating human-interpretable skill-based planning systems, and evaluating multilayered hybrid RL/control systems that bridge Technology Readiness Levels 2-3 to 4-5. Interns will code in Python, integrate work with compute platforms, modify Mobius Logic's deep reinforcement learning engines, participate in weekly project meetings, read and discuss research papers, present findings, and write research reports as part of a multidisciplinary team. Candidates must hold a Master's degree or higher in Computer Science, Engineering, Applied Mathematics, Data Science or related fields, possess a strong mathematical foundation with the ability to translate machine learning papers into pseudocode, be U.S. citizens, and ideally have experience in statistics, deep learning, control theory, and optionally operations research or reinforcement learning.