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Lean 4 Proof Engineer - Mathematical Formalization

Alignerr · Remote · Translation / linguistics

Lean 4 Proof Engineer - Mathematical Formalization is an open role at Alignerr, available remotely, with pay listed as $170–$200/hr.

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Applications open on Alignerr’s own site. How we make money.

At a glance

Pay
$170–$200/hrStated by Alignerr
Location
Remote
Listed
Since Oct 4, last confirmed Oct 23

How the pay compares

This role$200
Alignerr median$75
Science / STEM roles median$80
All roles median$60

Higher than 94% of the 117 distinct hourly rates advertised for Science / STEM roles right now. Hourly USD, top of each advertised range, one count per distinct rate. Same method as the Pay Index.

From Alignerr’s listing

“Lean 4 Proof Engineer — Mathematical Formalization About the Role What if your deep mathematical training could directly shape how AI understands and reasons about formal proof?”
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Lean 4 Proof Engineer — Mathematical Formalization About the Role What if your deep mathematical training could directly shape how AI understands and reasons about formal proof? We're looking for Lean 4 Proof Engineers to formalize advanced mathematics for cutting-edge AI research — translating rigorous human arguments into precise, machine-verifiable proofs that push the limits of what automated systems can express and verify. This is a fully remote, flexible contract role designed for mathematicians who find genuine satisfaction in the intersection of formal logic, proof theory, and modern proof assistants. 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 clarity, correctness, and reproducibility Analyze proofs across domains — identifying hidden assumptions, gaps, and formalizable sub-structures Construct formalizations that test and extend the limits of existing proof assistants, especially where automation breaks down Collaborate with AI researchers to design and refine formal verification pipelines Investigate failure modes of automated provers and articulate root causes — missing lemmas, library gaps, complexity barriers Provide expert guidance on proof decomposition, lemma selection, and structuring strategies for formal models Create 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 comparable formal systems — Lean 4 strongly preferred Deep enthusiasm for formal verification, proof assistants, and mechanized mathematics Able to take dense, human-wri

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Frequently asked questions

How do I apply for the Lean 4 Proof Engineer - Mathematical Formalization role at Alignerr?

Applications go through Alignerr's own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Alignerr's access model is "Open / self-serve" (Open roles / applications). This listing was first seen here on October 4, 2026 and was still live at the last check.

Is the Lean 4 Proof Engineer - Mathematical Formalization role at Alignerr remote?

Yes - Alignerr lists this role as remote.

What does the Lean 4 Proof Engineer - Mathematical Formalization role at Alignerr pay?

Alignerr lists this role's pay as $170–$200/hr, quoted from the listing rather than estimated.

What kind of work is Lean 4 Proof Engineer - Mathematical Formalization?

Translation / linguistics. AI training, evaluation, coding, writing and specialist review

Is Alignerr a legitimate AI-training platform?

Labelbox expert network - and that relationship is rated "Confirmed" here because it can be checked against labelbox.com rather than taken on Alignerr's word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.

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