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Applied Formal Methods Researcher (Lean 4)

Alignerr

AI & Data Training Contractor
Remote (Global) $170 – $200/hr May 25, 2026

Job description

Applied Formal Methods Researcher (Lean 4)

About the Role

What if your deep mathematical training could directly shape how AI understands and reasons about formal proofs? We're looking for mathematicians and formal methods experts to work at the cutting edge of AI research — translating rigorous mathematical arguments into machine-verifiable Lean 4 formalizations, and helping map the frontier of what automated reasoning can actually do.

This is a fully remote, flexible contract role designed for mathematicians who find beauty in precision and satisfaction in making a machine understand what a human mind can see.

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

What You'll Do

  • Translate informal mathematical proofs into clean, correct, and readable Lean 4 formalizations
  • Analyze proofs across domains — identifying hidden assumptions, gaps, and formalizable sub-structures
  • Construct formalizations that stress-test the limits of existing 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 researchers to design and evaluate strategies for improving formal verification pipelines
  • Develop reproducible proof scripts aligned with mathematical best practices and proof assistant idioms
  • Provide expert guidance on proof decomposition, lemma selection, and formal modelling strategies
  • Formalize classical results and compare machine-verifiable structures against textbook arguments
  • Uncover 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
  • Has a strong foundation in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematics
  • Has hands-on experience with Lean (Lean 3 or Lean 4), with Lean 4 strongly preferred
  • Deeply enthusiastic about formal verification, proof assistants, and the future of mechanized mathematics
  • Able to take a dense, informal argument and express it in a form that a machine can verify — cleanly and correctly
  • Mathematically mature, precision-driven, and genuinely excited by hard problems

Nice to Have

  • Experience with Coq, Isabelle/HOL, Agda, or comparable proof systems
  • 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 fails or requires extensive manual scaffolding
  • Prior experience with data annotation, evaluation systems, or AI training workflows
  • Strong communication skills for explaining formalization decisions, edge cases, and reasoning strategies

Why Join Us

  • Work on cutting-edge AI projects alongside leading AI research labs
  • Fully remote and flexible — work when and where it suits you
  • Freelance autonomy with the structure of meaningful, intellectually rigorous work
  • Contribute directly to how AI systems learn to reason about advanced mathematics
  • Potential for ongoing work and contract extension as new projects launch
  • Be part of a global collaboration at the intersection of mathematics, formal methods, and artificial intelligence
Apply now

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Frequently asked questions

Is Alignerr legitimate?
Yes, Alignerr is a legitimate company listed on RemoWork. We verify all companies on our platform. However, we always recommend doing your own research before sharing personal information or starting work. Check reviews from other users and visit the company's official website for the most up-to-date information.
How do I get started with Alignerr?
To get started, visit Alignerr's official website and look for their careers or sign-up page. Create an account, complete your profile with relevant skills and experience, and apply for available positions or projects. Some companies may require you to pass a qualification test or assessment before you can start working.
Does Alignerr offer remote work?
Yes, Alignerr offers remote work opportunities. Most tasks and positions can be completed from anywhere with a reliable internet connection and a computer or smartphone. Specific location requirements may vary by project or role, so check individual listings for details.
How does Alignerr pay its workers?
Payment methods and schedules vary. Common payment options include PayPal, bank transfers, Payoneer, and other digital payment platforms. Visit Alignerr's official website or check their payment/FAQ section for specific details about payment methods, minimum thresholds, and payout schedules.
What skills do I need to work with Alignerr?
Required skills vary by role and project. Generally, you'll need a reliable internet connection, basic computer literacy, strong attention to detail, and good communication skills. Some positions may require specialized expertise, language proficiency, or technical qualifications. Check individual job listings for specific requirements.
What type of AI work does Alignerr focus on?
Alignerr specializes in AI alignment and model evaluation tasks. Contributors help ensure AI models produce safe, accurate, and helpful outputs by evaluating model responses, identifying errors or biases, and providing human feedback that improves AI behavior and alignment with human values.
Do I need technical AI knowledge to work with Alignerr?
While technical AI knowledge is a plus, many Alignerr tasks are designed for non-technical contributors with strong critical thinking, writing, and analytical skills. The ability to evaluate content quality, identify factual errors, and provide clear feedback is more important than programming expertise for most roles.
How does Alignerr ensure quality in its work?
Alignerr implements multi-layered quality assurance including calibration tasks, consensus scoring between annotators, regular quality audits, and feedback loops. Contributors are expected to maintain high accuracy scores, and ongoing training helps maintain consistent quality standards.
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