Reasonable is the applied AI research company building formal verification for post-human software development.
Correctness guarantees for software developed by humans and machines are no longer impractical or prohibitively expensive. Code generated by AI can be provably correct, rather than plausibly functional. At Reasonable, we are doing the research, training the models, and developing the products required to make this a reality. Achieving this creates a new paradigm for high accountability software development and unlocks the full potential of AI for professional engineers.
We’re a compact, talent-dense technical team, with deep domain expertise in machine learning, formal verification and mathematical models of program semantics. Join us to develop the next frontier of formal reasoning and software engineering.
Proof follows function.
The Role
As a Student Researcher, you will be embedded within an industry-leading team, working on projects that contribute directly to our work on formally-grounded software development.
Our Student researcher programs vary in duration from 12-24 weeks, during which you’ll take on structured but challenging work. Projects are exploratory by design; open enough that you can bring a real intellectual contribution, structured enough that you deliver work worthy of open-source release or publication by the time you leave.
You Will
- Take on a focused research project that connects to Reasonable's core work in machine learning and formal verification. We will shape the project with you on arrival
- Run real experiments that produce results worth publishing or open-sourcing: training runs, verification benchmarks, theoretical work, product development contributions
- Work alongside Ferenc and the wider team of MTS in mutual exchange: you bring your area of expertise, we bring practical production and research experience
- Be in the room for technical conversations that go beyond your project
- Work on-site, alongside Ferenc and the founding technical team in Budapest
Requirements
- Currently enrolled in a PhD in Computer Science, Applied Mathematics, or a related technical field, and returning to your program after the internship
- Expertise in a relevant area of computer science, for example: Natural Language Understanding, Machine Learning (especially LLMs, RL, other post-training methods), Algorithmic Foundations of Optimization, Mathematical Models of Program Semantics, Programming Language Theory, or a related field
- Extra-curricular interest in the intersection of machine learning, formal methods, and software engineering, even if your current work doesn’t touch it
Bonus Points If You
- Have published at venues like NeurIPS, ICML, ICLR, POPL, PLDI, CAV, or similar
- Have open-source contributions in relevant projects: Lean, Dafny, Verus, training frameworks, or similar
- Are already working on the intersection of machine learning and formal methods
This is a serious research program. If you fit the profile, apply. If you know someone who would be ideal, we always reward great introductions.
Benefits
- Compensation: Generous compensation
- Location: Budapest, Hungary - we’re an on-site team
- World-class team and environment: an opportunity to build alongside deeply experienced founders, in a well-funded company that’s backed by industry leading VCs and angels including Oriol Vinyals, Zoubin Ghahramani, Jonathan Frankle and Guy Podjarny, amongst many others