<p><strong>.
Constructor Fabric turns a company's informal knowledge into production software through a pipeline of composable capability units called gears:
We are building a <strong>World Model</strong> over that pipeline — a model that does not just generate code, but predicts the <em>consequences</em> of an architectural decision: total cost of ownership, unintended side effects, latency and failure behaviour, and whether a proposed composition is even admissible.
<br>The central object is not a digital twin of the application but its <strong>formal architectural skeleton</strong>: gear contracts (GearSpec), a typed attributed hypergraph of the application (AppGraph), a composition algebra that defines which assemblies are legal, and several semantic projections (types, protocols, resources, security) over which properties are proved, refuted by counterexample, or reported as an explainable <strong>gap</strong>.
All of it is written in a <strong>domain-specific language</strong> whose syntax trees and graphs we keep in a projectional editor (JetBrains MPS) — so the DSL is not a convenience layer, it is the thing that defines the model's state space and the boundary of what the model is allowed to propose.
</p> <p><strong>Internship Role</strong></p> <p>As a Research Intern (Machine Learning for Software Engineering), you will support the data and modelling work behind the World Model — mining code, building datasets, and running baseline experiments.
We are looking for students who are keen to collaborate with industrial companies working with AI, open to out-of-the-box topics, and interested in a long-term collaboration.
</p> <p><strong>Key.