Remote Lean Formalization Specialist

Alignerr; Austin (CO); On-site; USD 83,000 - 165,000; Part time

Benefits offered by this job

Remote work

Flexible schedule

Freelance autonomy

Job summary

Alignerr is seeking a Mathematical Formalization Specialist to advance machine-verifiable mathematics using Lean and related proof systems. This fully remote, flexible hourly contract role collaborates with AI research teams to push the boundaries of formal verification and automated reasoning.

You will translate informal proofs into Lean, identify gaps, build formalizations, and craft readable proof scripts that align with best practices in proof assistants.

Qualifications

• Extensive experience writing rigorous formal proofs.
• Hands-on experience with Lean (Lean 3/4) and related proof systems.
• Ability to translate dense informal arguments into clean, machine-verifiable proofs.
• Interest in formal verification and AI research along with clear communication.

Responsibilities

• Translate informal mathematical proofs into Lean (and related proof systems).
• Analyze generic and domain-specific proofs to identify gaps and formalizable sub-structures.
• Build formalizations that stress-test the limits of existing proof assistants.
• Collaborate with researchers to improve formal verification pipelines.
• Develop readable, reproducible proof scripts aligned with best practices.
• Advise on proof decomposition, lemma selection, and structuring techniques.

Skills

Formal proof writing

Mathematical maturity

Translate informal proofs

Strong communication

Education

Master's degree or higher in Mathematics or related field

Tools

Lean (Lean 3/Lean 4)

Coq

Isabelle/HOL

Agda

Job description

Alignerr is seeking a Mathematical Formalization Specialist to advance machine-verifiable mathematics using Lean and related proof systems. This fully remote, flexible hourly contract role collaborates with AI research teams to push the boundaries of formal verification and automated reasoning.

You will translate informal proofs into Lean, identify gaps, build formalizations, and craft readable proof scripts that align with best practices in proof assistants.