We are building the trust stack for AI code generation, targeted at high-stakes computing where wrong numbers cost real money: a statically typed functional language designed for coding models, a compiler whose guarantees double as audit-grade trust infrastructure, and a coding model fine-tuned on the language with compiler fitness as the training signal. Language and model are co-designed.
Small team out of PyTorch, FAIR, and Meta. Strong institutional VC support from our pre-seed; active investor interest heading into seed.
Founding Research Scientist: own the agenda across the language and the model. Type system design, compiler analyses that produce useful fitness gradients, and fine-tuning open-weight coding models on a language with no pretraining footprint. Publish in PL and ML venues. Strongest fits cross between machine learning, programming languages, and formal methods. PhD preferred, equivalent output equally fine. Stack is Rust, Lean 4, Python.
Founding Engineering Lead: own engineering across compiler internals, language runtime, GPU backends, notebook and library tooling, and the AI infrastructure around training, evaluation, model release, and likely public inference. We already support x86 and ARM, CUDA and AMD, macOS and Linux, and the matrix grows. Real feel for statically typed functional programming expected; the kind of engineer who picks up Lean or Haskell on a weekend because they wanted to. Have led engineering before, formally or not. Stack is mostly Rust with Python where it earns its place, primarily on AWS.
Both roles: comfortable with early-stage ambiguity, define your own roadmap, defend it with evidence.
Apply: RS: https://wellfound.com/l/2Carrr Eng Lead: https://wellfound.com/l/2CewDC
Socials: YT: https://www.youtube.com/channel/UC4DLS_emqwKXO7A9qaOtxlw SS: https://burninai.substack.com/