Formal Verification Scientist (Lean 4 & Mathlib)
Top Benefits
About the role
About The Role
What if your deep mathematical training could directly shape how AI understands and reasons about formal proof? We're looking for mathematicians with hands-on Lean experience to formalize advanced mathematical arguments for cutting-edge AI research — translating human reasoning into precise, machine-verifiable knowledge.
This is a fully remote, flexible contract role. If you find satisfaction in taking a dense, elegant proof and expressing it in a form a machine can verify, this role was built for you.
Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week
What You'll Do
Translate informal mathematical proofs into Lean 4 with an emphasis on clarity, structure, and correctness Analyze generic and domain-specific proofs to identify gaps, hidden assumptions, and formalizable sub-structures Push the boundaries of existing proof assistants — working precisely where automated tools struggle or fail Collaborate with researchers to design, refine, and evaluate formal verification strategies Develop highly readable, reproducible proof scripts aligned with mathematical best practices and Lean idioms Advise on proof decomposition, lemma selection, and structuring techniques for formal models Formalize classical proofs and compare machine-verifiable structures against textbook arguments Investigate and articulate where and why automated provers break down — complexity, missing lemmas, library gaps Construct Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematics
Who You Are
Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field Strong foundation in rigorous proof writing across algebra, analysis, topology, logic, or discrete mathematics Hands-on experience with Lean (Lean 3 or Lean 4), Coq, Isabelle/HOL, Agda, or a comparable proof assistant — Lean strongly preferred Deep enthusiasm for formal verification, proof assistants, and the future of mechanized mathematics Able to translate informal mathematical arguments into clean, structured, machine-checkable formalizations A mathematically mature problem-solver who thrives at the frontier of what formal tools can express
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 where automated reasoning frequently fails or requires manual scaffolding Prior experience with data annotation, data quality, or evaluation systems Strong communication skills for explaining formalization decisions, edge cases, and reasoning strategies
Why Join Us
Work at the frontier of formal verification alongside world-leading AI research teams Fully remote and flexible — work when and where it suits you Freelance autonomy with the structure of meaningful, technically deep work Gain exposure to advanced LLMs and the processes behind how they are trained Contribute to research that is actively expanding what machines can understand and verify Potential for ongoing work and contract extension as new projects launch
Similar Jobs
Formal Verification Scientist (Lean 4 & Mathlib)
Top Benefits
About the role
About The Role
What if your deep mathematical training could directly shape how AI understands and reasons about formal proof? We're looking for mathematicians with hands-on Lean experience to formalize advanced mathematical arguments for cutting-edge AI research — translating human reasoning into precise, machine-verifiable knowledge.
This is a fully remote, flexible contract role. If you find satisfaction in taking a dense, elegant proof and expressing it in a form a machine can verify, this role was built for you.
Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week
What You'll Do
Translate informal mathematical proofs into Lean 4 with an emphasis on clarity, structure, and correctness Analyze generic and domain-specific proofs to identify gaps, hidden assumptions, and formalizable sub-structures Push the boundaries of existing proof assistants — working precisely where automated tools struggle or fail Collaborate with researchers to design, refine, and evaluate formal verification strategies Develop highly readable, reproducible proof scripts aligned with mathematical best practices and Lean idioms Advise on proof decomposition, lemma selection, and structuring techniques for formal models Formalize classical proofs and compare machine-verifiable structures against textbook arguments Investigate and articulate where and why automated provers break down — complexity, missing lemmas, library gaps Construct Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematics
Who You Are
Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field Strong foundation in rigorous proof writing across algebra, analysis, topology, logic, or discrete mathematics Hands-on experience with Lean (Lean 3 or Lean 4), Coq, Isabelle/HOL, Agda, or a comparable proof assistant — Lean strongly preferred Deep enthusiasm for formal verification, proof assistants, and the future of mechanized mathematics Able to translate informal mathematical arguments into clean, structured, machine-checkable formalizations A mathematically mature problem-solver who thrives at the frontier of what formal tools can express
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 where automated reasoning frequently fails or requires manual scaffolding Prior experience with data annotation, data quality, or evaluation systems Strong communication skills for explaining formalization decisions, edge cases, and reasoning strategies
Why Join Us
Work at the frontier of formal verification alongside world-leading AI research teams Fully remote and flexible — work when and where it suits you Freelance autonomy with the structure of meaningful, technically deep work Gain exposure to advanced LLMs and the processes behind how they are trained Contribute to research that is actively expanding what machines can understand and verify Potential for ongoing work and contract extension as new projects launch