Lean 4 Proof Engineer - Mathematical Formalization

* Translate informal mathematical proofs into Lean 4 (and related proof systems) with an emphasis on clarity, structure, and correctness * Analyze both generic and domain-specific proofs — identifying gaps, hidden assumptions, and formalizable sub-structures * Push the limits of existing proof assistants by constructing formalizations where tools struggle or fail * Collaborate with AI researchers to design, refine, and evaluate formal verification strategies * Develop highly readable, reproducible proof scripts aligned with mathematical best practices and proof assistant idioms * Provide expert guidance on proof decomposition, lemma selection, and structuring techniques * Formalize classical proofs and compare machine-verifiable structures against textbook arguments * Articulate and document where and why automated provers break down — complexity, missing lemmas, insufficient libraries, and more * Surface deeper patterns or generalizations implicit in the original mathematics through your Lean formalizations

Who You Are

* Hold a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field * Possess a strong foundation in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematics * Have hands-on experience with Lean (Lean 3 or Lean 4), Coq, Isabelle/HOL, Agda, or comparable systems — Lean strongly preferred * Deeply enthusiastic about formal verification, proof assistants, and the future of mechanized mathematics * Able to translate informal arguments into clean, well-structured formal proofs * A mathematically mature problem-solver who finds satisfaction in taking a dense, elegant argument and expressing it in a form a machine can verify

Nice to Have

* Familiarity with type theory, the Curry-Howard correspondence, and proof automation tools * Experience with large-scale formalization projects such as Mathlib * Exposure to theorem provers where automated reasoning frequently fails or requires manual scaffolding * Prior experience with data annotation, data quality, or AI evaluation systems * Strong written communication skills for documenting formalization decisions, edge cases, and reasoning strategies

Why Join Us

* Work on frontier AI projects alongside leading research labs and teams * Fully remote and flexible — structure your work around your schedule * Freelance autonomy with intellectually stimulating, high-impact work * Gain rare exposure to how advanced AI models are trained on formal mathematical reasoning * Contribute to expanding the boundary of what machines can formally understand and verify * Potential for ongoing work and contract extension as new projects launch