Founding Researcher

## About the role AI will write most of the world's code. The interesting bottleneck is no longer whether a model can produce code; it is whether anyone should trust it. We are building the trust stack for AI code generation, targeted at high-stakes computing where wrong numbers cost real money. A statically typed language designed for coding models. A compiler whose guarantees double as audit-grade trust infrastructure. A coding model fine-tuned on the language, with the compiler emitting the continuous fitness signal that becomes its training reward. Language and model are co-designed. We are a small team out of PyTorch, FAIR, NYU, & KCL, with strong institutional VC support from our pre-seed and active investor interest heading into seed. ## What you will do - Own the research agenda across the language and the model. - Design type systems and compiler analyses that yield useful fitness gradients. - Fine-tune coding models on a language with no pretraining footprint, including the supervised and reinforcement stages that build competence from zero. - Publish in PL and ML venues. - Choose the next experiments. - Help shape what verified computing looks like in the wild. ## Who you are - You publish at top venues in some combination of machine learning, programming languages, or formal methods. People who cross between them are who we want most. - PhD preferred, equivalent output equally fine. - You can talk fluently about both training dynamics and type systems. You do not need expertise in both, but you need real curiosity about whichever one you arrive without. - You write production code. Our stack is Rust and Python. - You are comfortable with early-stage ambiguity. The team is small. You will define your own roadmap and defend it with evidence. ## Why join us? - You work directly with founders who have built infrastructure used across the industry. - The research is well-scoped, the commercial wedge is real, and the equity reflects how early it is. ## Location London or New York, partially on-site