Lean 4 Proof Engineer - Mathematical Formalization
Alignerr Corp.
Lean 4 Proof Engineer - Mathematical Formalization
What if your mathematical expertise could directly shape how AI understands and reasons about formal proof? We're looking for skilled mathematicians to translate rigorous human arguments into machine-verifiable Lean 4 formalizations — working at the very frontier of what proof assistants can express, capture, and automate.This is a fully remote, flexible contract role built for mathematicians who care deeply about formal verification and want their work to matter at the cutting edge of AI research.Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 10-40 hours/weekWhat You'll Do
Translate informal mathematical proofs into precise, machine-verifiable Lean 4 formalizations with an emphasis on clarity, structure, and correctnessAnalyze generic and domain-specific proofs to identify gaps, hidden assumptions, and formalizable sub-structuresConstruct formalizations that push the limits of existing proof assistants - especially where automation fails or falls shortCollaborate with researchers to design, refine, and evaluate strategies for improving formal verification pipelinesDevelop readable, reproducible proof scripts aligned with mathematical best practices and Lean idiomsProvide expert guidance on proof decomposition, lemma selection, and structuring techniques for formal modelsInvestigate where automated provers break down and articulate precisely why - complexity, missing lemmas, library gaps, and beyondCreate Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematicsWho You Are
Hold a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related fieldHave a strong foundation in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematicsHave hands-on experience with Lean (Lean 3 or Lean 4), Coq, Isabelle/HOL, Agda, or comparable proof systems - Lean strongly preferredDeeply enthusiastic about formal verification, proof assistants, and the future of mechanized mathematicsAble to take a dense, elegant human argument and express it in a form a machine can verify - cleanly and completelyNice to Have
Familiarity with type theory, the Curry-Howard correspondence, and proof automation toolsExperience contributing to large-scale formalization projects such as MathlibExposure to theorem provers where automated reasoning frequently fails or requires significant manual scaffoldingPrior experience with data annotation, data quality, or evaluation systemsStrong communication skills for explaining formalization decisions, edge cases, and reasoning strategiesWhy Join Us
Work on genuinely frontier AI research alongside leading labs and research teamsFully remote and flexible - work when and where it suits youFreelance autonomy with the structure of intellectually stimulating, high-impact workContribute directly to expanding what formal verification can express and automatePotential for ongoing work and contract extension as new projects launch
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What if your mathematical expertise could directly shape how AI understands and reasons about formal proof? We're looking for skilled mathematicians to translate rigorous human arguments into machine-verifiable Lean 4 formalizations — working at the very frontier of what proof assistants can express, capture, and automate.This is a fully remote, flexible contract role built for mathematicians who care deeply about formal verification and want their work to matter at the cutting edge of AI research.Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 10-40 hours/weekWhat You'll Do
Translate informal mathematical proofs into precise, machine-verifiable Lean 4 formalizations with an emphasis on clarity, structure, and correctnessAnalyze generic and domain-specific proofs to identify gaps, hidden assumptions, and formalizable sub-structuresConstruct formalizations that push the limits of existing proof assistants - especially where automation fails or falls shortCollaborate with researchers to design, refine, and evaluate strategies for improving formal verification pipelinesDevelop readable, reproducible proof scripts aligned with mathematical best practices and Lean idiomsProvide expert guidance on proof decomposition, lemma selection, and structuring techniques for formal modelsInvestigate where automated provers break down and articulate precisely why - complexity, missing lemmas, library gaps, and beyondCreate Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematicsWho You Are
Hold a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related fieldHave a strong foundation in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematicsHave hands-on experience with Lean (Lean 3 or Lean 4), Coq, Isabelle/HOL, Agda, or comparable proof systems - Lean strongly preferredDeeply enthusiastic about formal verification, proof assistants, and the future of mechanized mathematicsAble to take a dense, elegant human argument and express it in a form a machine can verify - cleanly and completelyNice to Have
Familiarity with type theory, the Curry-Howard correspondence, and proof automation toolsExperience contributing to large-scale formalization projects such as MathlibExposure to theorem provers where automated reasoning frequently fails or requires significant manual scaffoldingPrior experience with data annotation, data quality, or evaluation systemsStrong communication skills for explaining formalization decisions, edge cases, and reasoning strategiesWhy Join Us
Work on genuinely frontier AI research alongside leading labs and research teamsFully remote and flexible - work when and where it suits youFreelance autonomy with the structure of intellectually stimulating, high-impact workContribute directly to expanding what formal verification can express and automatePotential for ongoing work and contract extension as new projects launch
#J-18808-Ljbffr
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