About the job
About Us
Axiomatic AI is at the forefront of developing innovative AI systems that leverage the scientific method's precision. By marrying deep learning with formal logic and physics-based modeling, we are crafting AI systems that are not only verifiable and interpretable but also enhance the capabilities of human researchers in critical scientific and engineering domains.
Our ambitious mission, known as 30×30, aims to achieve a 30-fold enhancement in the speed, accessibility, and cost of semiconductor and photonic hardware development by the year 2030.
We are dedicated to transforming hardware design and simulation across these fields and are assembling a team of driven professionals to transition these groundbreaking ideas from research into tangible commercial products.
Position Overview
We are seeking a Research Intern who is passionate about the interplay between formal methods, artificial intelligence, and scientific reasoning. This internship is particularly well-suited for PhD candidates eager to propel AI forward in the realms of science and engineering while developing tools to validate scientific outputs.
The focus of this internship will be on groundbreaking research into automated verification of scientific reasoning. You will contribute to the architecture of AI agents designed to navigate intricate mathematical formalization workflows, develop knowledge retrieval systems, and engage in both formal and informal verification methodologies.
Your Mission
- Contribute to the development of tools for formal reasoning, including enhanced library search, theorem retrieval systems, and formalization assistants.
- Investigate applications of Lean metaprogramming or similar formal methods tools.
- Design verification pipelines for intricate equation derivations, establishing a foundational interface between AI and physics.
- Collaborate closely with researchers to pinpoint a feasible and impactful project that advances both our internal capabilities and the understanding of our research.
- Conduct exemplary scientific work that has the potential for publication in top-tier venues (ICLR, ICML, NeurIPS, etc.) or contribute to significant open-source projects.

