Burnin

We are building the trust stack for AI code generation for 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 with strong VC support. 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; PhD preferred. Founding Engineering Lead: own engineering across compiler internals, language runtime, GPU backends, notebook and library tooling, and AI infrastructure around training, evaluation, model release, and likely public inference. Experience with statically typed functional programming (e.g., picking up Lean or Haskell), has led engineering before. Stack 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.