Jobs · Analyst

Researcher - Lean 4 & Formal Proof Systems

Alignerr · Chicago, IL · Today
RemoteRemoteAnalystContract

What You'll Do

  • Translate informal mathematical proofs into Lean 4 (and related systems) with a focus on clarity, structure, and correctness
  • Analyze proofs across domains — identifying gaps, hidden assumptions, and formalizable sub-structures
  • Create formalizations that test and push the limits of existing proof assistants, especially where automation fails
  • Investigate why automated provers break down — complexity, missing lemmas, insufficient libraries — and document your findings
  • Develop clean, reproducible proof scripts aligned with mathematical best practices and Lean idioms
  • Advise on proof decomposition, lemma selection, and structuring strategies for formal models
  • Collaborate with researchers to design and evaluate approaches for improving formal verification pipelines
  • Create Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematics

Who You Are

  • Hold a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field
  • Have 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 formal systems — Lean 4 strongly preferred
  • Be deeply passionate about formal verification, proof assistants, and the future of mechanized mathematics
  • Able to translate dense, informal mathematical arguments into clean, structured, machine-verifiable proofs
  • Be comfortable working independently and asynchronously in a remote environment

Nice to Have

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

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