Student Researcher Program

Reasonable is an applied research company developing training paradigms for superhuman programming AI. We draw on on domain expertise in machine learning, formal verification and mathematical models of program semantics to create open-ended training environments, pre-training datasets, and post-training methods that continue to challenge LLMs even as they develop deeply superhuman levels of understanding and skill in programming.

We think when AI writes most code, it will ultimately enable new ways for computers to be programmed, using programming paradigms that are just too challenging for humans to use at scale, enabling optimisations that were previously too complex to attempt. We call these applications post-human software engineering.

As a Student Researcher, you will have the opportunity to participate in research projects that push the frontiers of artificial intelligence –and its applications for social good. Student Researcher projects are exploratory and experiences that drive scientific advancement across a multitude of research areas. Students will work collaboratively on projects that explore innovative research challenges and support the creation of breakthrough technologies. Projects vary in duration based on team and student requirements.

This opportunity is intended for students who are pursuing a PhD degree program in Computer Science or a related field.

Requirements

• Currently enrolled in a PhD degree in Computer Science, Applied Mathematics, or related technical field.
• Experience in one area of computer science (e.g., Natural Language Understanding, Machine Learning, Deep Learning, Algorithmic Foundations of Optimization, Quantum Information Science, Data Science, Software Engineering, or similar areas).
• Currently enrolled in a full-time degree program and returning to the program after completion of the internship.

Nice to have:

• Experience as a researcher, including internships, full-time, or at a lab.
• Open source contributions.
• Experience in either Machine Learning (preferably language models, reinforcement learning) or Formal Verification (SMT-based or interactive theorem provers).
• Experience contributing to research communities or efforts, including publishing papers in major conferences or journals.

Duration: Between 12 and 24 weeks, with a minimum time commitment of four days a week.

Location: In-person in Budapest, Hungary for the duration of the engagement.

Benefits

• Competitive salary

• Flexible working patterns