Company Description: Millennium Research is dedicated to advancing the frontier of mathematics through the application of artificial intelligence. The organization focuses on machine-verified mathematics, developing systems to fundamentally change how mathematical reasoning models approach high-level problems. Team members collaborate across formal methods, competition-level mathematics, and modern AI to build tools that deepen theoretical understanding and enable new discoveries. Millennium Research offers a highly technical, research-focused environment for professionals interested in the intersection of advanced mathematics and machine learning.
Role Description: This is an on-site contract role for a Machine Learning Engineer (Formal Reasoning) based in New York, NY. You'll help build the training data and evaluation systems behind our agentic AI for mathematics the pipeline that teaches models to reason about mathematics with machine-checked rigor. Day-to-day work spans generating and curating high-quality formal reasoning data (translating mathematics from LaTeX into Lean 4 / mathlib), building and refining the agentic pipeline that drafts, checks, and ranks candidate formalizations, and verifying that model outputs are actually correct and faithful not just plausible. You'll work at the intersection of large language models, formal verification, and mathematical reasoning, collaborating with researchers to turn hard mathematical problems into signals our systems can learn from, and keeping the codebase and results reproducible.
Qualifications
Hands-on experience with a proof assistant, Lean 4 strongly preferred (Coq, Isabelle, or Agda also welcome); alternatively, a strong pure-mathematics background with the ability to become productive in Lean quickly.
Real mathematical maturity the ability to read a theorem from a paper and know precisely what it claims. A competition background (Putnam, IMO, or similar) is a significant plus.
Proficiency in Python; familiarity with LLM or agentic tooling (tool use, orchestration, evaluations) is beneficial for the AI-systems work.
Solid foundation in Computer Science and algorithms, with the ability to write efficient, well structured code.
Bachelor's or advanced degree (or in progress) in Mathematics, Computer Science, or a related quantitative field, or equivalent practical experience.
Ability to work collaboratively in a research-oriented environment and communicate complex technical concepts clearly.
Contributions to mathlib, experience with proof automation or tactic writing, or a background in autoformalization / ML-for-math is highly beneficial.

Machine Learning Engineer

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