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Applied Formal Methods Researcher (Lean 4)

Alignerr
Department:Data Analysis
Type:REMOTE
Region:Australia
Location:Sydney, New South Wales, Australia
Experience:Entry level
Estimated Salary:A$50,000 - A$100,000
Skills:
LEAN 4FORMAL VERIFICATIONPROOF ASSISTANTSMATHEMATICSLOGICTYPE THEORYCOQISABELLE/HOLAGDA
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Job Description

Posted on: May 25, 2026

About The Role What if your deep mathematical training could directly shape how AI understands and constructs formal proofs? We're looking for mathematicians with hands-on experience in formal verification to work at one of the most exciting intersections in modern AI research — mechanized mathematics. You'll translate rigorous human-written arguments into machine-verifiable Lean 4 proofs, explore the boundaries of what automated provers can and cannot do, and help build the datasets that train the next generation of AI reasoning systems. This is a fully remote, flexible contract role designed for mathematicians who love precision, structure, and the intellectual challenge of formal verification.

  • Organization: Alignerr
  • Type: Hourly Contract
  • Location: Remote
  • Commitment: 10–40 hours/week

What You'll Do

  • Translate informal mathematical proofs into clean, structured Lean 4 formalizations with an emphasis on correctness and readability
  • Analyze proofs across domains — identifying hidden assumptions, logical gaps, and formalizable sub-structures
  • Construct formalizations that probe and test the limits of existing proof assistants, particularly where automation breaks down
  • Investigate why automated provers fail on specific problems — whether due to complexity, missing lemmas, or insufficient libraries — and document your findings clearly
  • Collaborate with AI researchers to refine strategies for improving formal verification pipelines
  • Provide expert guidance on proof decomposition, lemma selection, and structuring techniques for formal models
  • Develop highly reproducible proof scripts aligned with mathematical best practices and Lean idioms
  • Create Lean proofs that expose deeper patterns or generalizations implicit in the original mathematics

Who You Are

  • Holder of a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field
  • Strong foundation in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematics
  • Hands-on experience with Lean (Lean 3 or Lean 4), with Lean 4 strongly preferred — experience with Coq, Isabelle/HOL, or Agda also considered
  • Deep enthusiasm for formal verification, proof assistants, and the future of mechanized mathematics
  • Able to translate dense, informal mathematical arguments into precise, machine-checkable formal proofs
  • Self-directed and comfortable working independently in an asynchronous, 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 where automated reasoning frequently requires manual scaffolding
  • Prior experience with data annotation, evaluation systems, or AI training workflows
  • Strong communication skills for articulating formalization decisions, edge cases, and reasoning strategies

The Ideal Candidate You're a mathematically mature problem-solver who thrives at the frontier of formal verification. You find genuine satisfaction in taking a dense, elegant human argument and expressing it in a form a machine can verify. You appreciate precision and structural beauty — and you're energized by the challenge of resolving gaps that automated tools cannot yet bridge. Why Join Us

  • Work directly on cutting-edge AI research projects alongside leading AI labs
  • Fully remote and flexible — work when and where it suits you
  • Freelance autonomy: set your own pace within a 10–40 hour weekly range
  • Gain rare exposure to how advanced AI reasoning systems are built and trained
  • Contribute to research that is actively pushing the frontier of what machines can prove
  • Potential for ongoing work and contract extension as new projects launch
Originally posted on LinkedIn

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