Mathematical Formalization Specialist
Alignerr • Remote • Posted 0 days ago
Education
Any
Type
Pay Rate
$100/task
Posted
0d ago
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About this Role
What You'll Do
- Translate informal mathematical proofs into Lean (and related proof systems) with a focus on clarity, structure, and correctness
- Analyze proofs across domains — identifying gaps, hidden assumptions, and formalizable sub-structures
- Construct formalizations that push the limits of existing proof assistants, especially where automation breaks down
- Collaborate with researchers to design and refine strategies for improving formal verification pipelines
- Develop clean, readable, and reproducible proof scripts aligned with mathematical best practices
- Provide expert guidance on proof decomposition, lemma selection, and structuring techniques for formal models
- Investigate and articulate why automated provers fail on specific problems — complexity, missing lemmas, insufficient libraries, or otherwise
About the Role
What if your deep mathematical training could directly shape how AI reasons, verifies, and understands formal logic? We're looking for expert mathematicians to translate advanced human-written proofs into machine-verifiable formalizations — working at the exact boundary of what modern proof assistants can and cannot yet do. This is a fully remote, flexible contract role built for mathematicians who are passionate about rigorous proof construction and formal verification. If you find satisfaction in taking a dense, elegant argument and expressing it with the precision a machine can verify, this role was made for you.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: Flexible
Who You Are
- Hold a Master's degree or higher in Mathematics, Logic, Theoretical Computer Science, or a closely related field
- Deeply fluent in rigorous proof writing across areas such as algebra, analysis, topology, logic, or discrete mathematics
- Have hands-on experience with Lean (Lean 3 or Lean 4) — or comparable systems like Coq, Isabelle/HOL, or Agda — with Lean strongly preferred
- Genuinely enthusiastic about formal verification, proof assistants, and the future of mechanized mathematics
- Able to translate informal arguments into well-structured, machine-verifiable proofs with minimal scaffolding
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 proving contexts where automated reasoning frequently requires manual intervention
- Strong communication skills for explaining formalization decisions, edge cases, and proof strategies to collaborators
Sample Tasks
- Formalize classical proofs and compare machine-verifiable structures against textbook arguments
- Identify where automated provers break down and document why — feeding directly into AI research
- Create Lean proofs that surface deeper patterns or generalizations implicit in the original mathematics
Why Join Us
- Work at the frontier of AI research alongside leading labs advancing model reasoning and reliability
- Fully remote and asynchronous — work when and where it suits you
- Freelance autonomy with the structure of meaningful, high-impact technical work
- Your expertise directly shapes the next generation of AI formal reasoning capabilities
- Potential for ongoing work and contract extension as new projects launch
Requirements
- Fluent proficiency in English (Written & Verbal)
- Reliable high-speed internet connection
- Bachelor's degree or equivalent professional experience
- Demonstrated expertise in Mathematics
Compensation Analysis
What if your deep mathematical training could directly shape how AI reasons, verifies, and understands formal logic? We're looking for expert mathematicians to translate advanced human-written proofs into machine-verifiable formalizations — working at the exact boundary of what modern proof assistants can and cannot yet do. This is a fully remote,
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Frequently Asked Questions
What is the assessment actually like?
Notoriously strict. Alignerr uses TestGorilla for role-specific timed tests — a blank coding environment for engineers, rigorous grammar and fact-checking for writers. There is almost no hand-holding. The critical catch: this is essentially a one-shot process. Fail or abandon the assessment, and you are typically locked out of that role permanently with no option to retake.
How quickly can I start earning after I pass?
Not immediately. Even after passing the assessment and completing identity verification (via Persona) and billing setup (via Deel), you may sit in a waiting pool for weeks or months. You only start earning when a project matching your specific skills launches and you are officially assigned. Do not plan around Alignerr income until you are actively on a project.
Is there a community?
Yes — and it is one of Alignerr's genuine strengths. Once assigned to a project, you are added to Slack channels where you can ask questions, get rubric clarifications from admins, and talk to other AI trainers. This is rare in AI training and makes a real difference when guidelines are ambiguous or change mid-project.
Is this traditional consulting?
Not exactly. You act as a "Teacher" for advanced AI. Instead of client deliverables, you are given complex scenarios to evaluate. You grade the AI's logic, correct its hallucinations, and provide expert-level reasoning. Your job is to train the model to think like you do.
Why is the pay so high?
This role requires deep, verified expertise. General knowledge isn't enough; the model is specifically being trained on "edge cases"—the rare, difficult, or highly technical nuances that only a senior professional would know.
What is the workload like?
This is cognitive, deep work. Unlike simple data labeling, you might spend 45-60 minutes on a single task, researching citations or verifying complex calculations. Quality is prioritized over speed.
What is the barrier to entry?
Alignerr is known for difficult technical assessments. You must pass a timed test in your specific domain (e.g., Python, Physics, or Language) before you are eligible for any paid projects.