About the role
Mathematical Formalization Specialist (Lean / Formal Proof Systems)
About Alignerr
Alignerr partners with leading AI labs to build expert-driven workflows that improve model reasoning. We recruit top mathematicians and specialists to solve tasks where automated tools fail, advancing AI reliability, formalization, and high-integrity dataset creation.
Role Overview
We are seeking a mathematician with deep training in rigorous proof construction and hands-on experience with formal proof languages—especially Lean. This role sits at the intersection of mathematics and computer science, focusing on translating human-written mathematical arguments into precise, machine-verifiable formalizations. You will work on proofs that often lie beyond the current capabilities of automated provers, helping us map the frontier of what formal verification can express, capture, and automate.
What You’ll Do
- Translate informal mathematical proofs into Lean (and related proof systems) with an emphasis on clarity, structure, and correctness.
- Analyze generic and domain-specific proofs, identifying gaps, hidden assumptions, and formalizable sub-structures.
- Construct formalizations that test the limits of existing proof assistants—especially where tools struggle or fail.
- Collaborate with researchers to design, refine, and evaluate strategies for improving formal verification pipelines.
- Develop highly 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.
What You Bring
Must-Have:
- 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.
Nice-to-Have:
- Familiarity with type theory, Curry–Howard correspondence, and proof automation tools.
- Experience with large-scale formalization projects (e.g., mathlib).
- Exposure to theorem provers where automated reasoning frequently fails or requires manual scaffolding.
- Strong communication skills for explaining formalization decisions, edge cases, and reasoning strategies.
Ideal Candidate
A mathematically mature problem-solver who enjoys working at the frontier of formal verification—someone who finds satisfaction in taking a dense, elegant human argument and expressing it in a form that a machine can understand. You appreciate precision, structural beauty, and the challenge of resolving gaps that automated tools cannot yet bridge.
Sample Work You Might Do
- Formalize classical proofs and compare machine-verifiable structures against textbook arguments.
- Investigate where automated provers break down, and articulate why (complexity, missing lemmas, insufficient libraries, etc.).
- Create Lean proofs that reveal deeper patterns or generalizations implicit in the original mathematics.
Aplyr's read
Labelbox is a cutting-edge data training platform focused on enhancing AI capabilities through efficient data annotation. Ideal for tech professionals passionate about AI and machine learning.
What's promising
- •Labelbox offers a robust platform that significantly accelerates AI model training.
- •The company is at the forefront of AI data annotation, a rapidly growing field.
- •Recent roles indicate a strong focus on diverse AI applications and research.
What to watch
- •The niche focus on data annotation may limit broader tech career opportunities.
- •Highly specialized roles might require advanced expertise in AI and machine learning.
- •Potential candidates may face intense competition due to the company's innovative reputation.
Why Labelbox
- •Labelbox uniquely integrates data annotation with AI model improvement.
- •The company emphasizes a forward-deployed engineering approach, embedding engineers directly with clients.
- •Its platform is designed to streamline complex data labeling processes efficiently.
Aplyr’s read is generated by AI from public sources. Was it useful?
About Labelbox
Labelbox is a data training platform that enables organizations to build and manage high-quality training datasets for machine learning applications. By streamlining the data labeling process, Labelbox empowers teams to accelerate their AI initiatives and improve model performance.
