Researcher - Lean 4 & Formal Proof Systems

Overview

Researcher - Lean 4 & Formal Proof Systems ation:
Remote (Glasgow, Scotland, United Kingdom is listed in the original but this role is described as remote).

• Write and formalize advanced mathematical proofs in Lean for cutting-edge AI research, focusing on formal verification and the capabilities of modern proof assistants.
• Translate informal mathematical proofs into Lean (and related proof systems) with emphasis on clarity, structure, and correctness.
• Analyze generic and domain-specific proofs, identify gaps, hidden assumptions, and formalizable sub-structures.
• Construct formalizations that test the limits of existing proof assistants, particularly where tools struggle or fail.
• Collaborate with researchers to design, refine, and evaluate strategies for improving formal verification pipelines.
• Develop readable, reproducible proof scripts aligned with mathematical best practices and proof assistant idioms.
• Provide guidance on proof decomposition, lemma selection, and structuring techniques for formal models.
• Explore classical proofs, compare machine-verifiable structures against textbook arguments, and document findings.
• Create Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematics.

• Master’s degree (or higher) in Mathematics, Logic, Theoretical Computer Science, or a closely related field.
• Strong foundation in rigorous proof writing and mathematical reasoning across areas such as algebra, analysis, topology, logic, or discrete math.
• Hands-on experience with Lean (Lean 3 or Lean
  4), Coq, Isabelle/HOL, Agda, or comparable systems, with Lean strongly preferred.
• Deep enthusiasm for formal verification, proof assistants, and the future of mechanized mathematics.
• Ability to translate informal arguments into clean, structured formal proofs.
• Preferred: experience with data annotation, data quality, or evaluation systems; familiarity with type theory, Curry-Howard correspondence, and proof automation tools; experience with large-scale formalization projects (e.g., mathlib).

• Competitive pay and flexible remote work.
• Collaborate with a team working on cutting-edge AI projects.
• Exposure to advanced LLMs and how they’re trained.
• Freelance perks: autonomy, flexibility, and global collaboration.
• Potential for contract extension.

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

Notes:
This description reflects the information present in the original posting. Applications may include related steps such as submitting a resume, completing a screening, and project matching.