Applied Formal Methods Researcher (Lean 4)
Alignerr, Denver, CO, United States
About The Role
What if your deep mathematical expertise could directly shape how AI reasons about the hardest problems in formal verification? We’re looking for Applied Formal Methods Researchers to translate rigorous mathematical arguments into machine-verifiable Lean 4 proofs — working at the exact frontier where human mathematical intuition meets the limits of automated reasoning.
This is a fully remote, flexible contract role for mathematicians who thrive on precision, love proof assistants, and want their work to matter at the cutting edge of AI research.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You’ll Do
Translate informal mathematical proofs into clean, correct, machine-verifiable Lean 4 formalizations
Analyze proofs across domains — identifying gaps, hidden assumptions, and formalizable sub-structures
Construct formalizations that stress-test the limits of modern proof assistants, especially where automation breaks down
Investigate and articulate why automated provers fail — whether due to complexity, missing lemmas, or library gaps
Collaborate with AI researchers to design and refine formal verification pipelines
Develop highly readable, reproducible proof scripts aligned with mathematical best practices
Guide proof decomposition strategies, lemma selection, and formal model structuring
Formalize classical results and compare machine-verifiable structures against textbook arguments
Surface deeper patterns or generalizations implicit in the original mathematics
Who You Are
Holds a Master’s degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field
Deeply comfortable with 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 comparable proof assistants — Lean strongly preferred
Genuinely excited about formal verification, proof assistants, and the future of mechanized mathematics
Able to take dense, informal arguments and express them with machine‑level precision
A mathematically mature problem‑solver who finds satisfaction in resolving the gaps automated tools cannot yet bridge
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 regimes where automated reasoning frequently requires manual scaffolding
Prior experience with data annotation, evaluation systems, or AI training workflows
Strong communication skills for explaining formalization decisions and reasoning strategies
Why Join Us
Work directly on cutting‑edge AI research projects alongside leading research labs
Fully remote and flexible — work when and where it suits you
Freelance autonomy with the structure of meaningful, technically demanding workContribute to defining what the next generation of mechanized mathematics can express and automate
Potential for ongoing work and contract extension as new projects launch
#J-18808-Ljbffr
What if your deep mathematical expertise could directly shape how AI reasons about the hardest problems in formal verification? We’re looking for Applied Formal Methods Researchers to translate rigorous mathematical arguments into machine-verifiable Lean 4 proofs — working at the exact frontier where human mathematical intuition meets the limits of automated reasoning.
This is a fully remote, flexible contract role for mathematicians who thrive on precision, love proof assistants, and want their work to matter at the cutting edge of AI research.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You’ll Do
Translate informal mathematical proofs into clean, correct, machine-verifiable Lean 4 formalizations
Analyze proofs across domains — identifying gaps, hidden assumptions, and formalizable sub-structures
Construct formalizations that stress-test the limits of modern proof assistants, especially where automation breaks down
Investigate and articulate why automated provers fail — whether due to complexity, missing lemmas, or library gaps
Collaborate with AI researchers to design and refine formal verification pipelines
Develop highly readable, reproducible proof scripts aligned with mathematical best practices
Guide proof decomposition strategies, lemma selection, and formal model structuring
Formalize classical results and compare machine-verifiable structures against textbook arguments
Surface deeper patterns or generalizations implicit in the original mathematics
Who You Are
Holds a Master’s degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field
Deeply comfortable with 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 comparable proof assistants — Lean strongly preferred
Genuinely excited about formal verification, proof assistants, and the future of mechanized mathematics
Able to take dense, informal arguments and express them with machine‑level precision
A mathematically mature problem‑solver who finds satisfaction in resolving the gaps automated tools cannot yet bridge
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 regimes where automated reasoning frequently requires manual scaffolding
Prior experience with data annotation, evaluation systems, or AI training workflows
Strong communication skills for explaining formalization decisions and reasoning strategies
Why Join Us
Work directly on cutting‑edge AI research projects alongside leading research labs
Fully remote and flexible — work when and where it suits you
Freelance autonomy with the structure of meaningful, technically demanding workContribute to defining what the next generation of mechanized mathematics can express and automate
Potential for ongoing work and contract extension as new projects launch
#J-18808-Ljbffr