Our Mission
Advancing AI R&D
for the good of all.
Building intelligent systems that collaborate with humans to accelerate scientific discovery.
Thesis is building an intelligent platform that collaborates with humans to invent, train, and refine machine learning models.
We focus first and foremost on machine learning as it exists in the real world: iterative, experimental, and performance-driven. From insurance to quantitative finance to high-impact production ML systems, progress today depends on fragmented tools and individual intuition. Thesis turns that process into a systematic, AI-native loop that compounds over time.
Our mission is to put frontier machine learning in the hands of every researchers and engineers in a shared environment where human judgment and intelligent systems work together to deliver real, measurable improvements.
Machine Learning R&D
Machine learning advances through iteration: choosing data, designing architectures, running experiments, interpreting results, and deciding what to try next. There is no closed-form theory for this process. Progress comes from making good decisions under uncertainty.
Thesis systematizes this loop.
We are building systems that collaborate with humans end-to-end to invent models, train them at scale, debug failures, and refine performance. The platform observes experiments, decisions, and outcomes, learning which actions lead to real gains and which do not. Over time, this turns hard-won engineering intuition into reusable signal.
Benchmarks such as OpenAI's MLE-Bench and METR's RE-Bench point toward the next phase of ML: agents that don't just write code, but conduct machine learning research. Thesis is the environment where those agents operate alongside humans.
The Vision
In the limit, Thesis becomes the default environment for applied machine learning.
Any team should be able to run thousands or millions of experiments in silico, guided by intelligent agents that reason over data, models, and prior results. Machine learning progress should compound across projects, organizations, and industries.
As ML becomes the substrate for decision-making in finance, insurance, and every data-driven system, the way we build and improve models will determine who moves fastest. Our goal is to define the platform that makes that progress systematic.
Join Us
Help build the future
of ML research.
We're hiring researchers, engineers, and builders.