Internship position for World Models for Software Architecture (Constructor Fabric)

About the Project

Constructor Fabric turns a companys informal knowledge into production software through a pipeline of composable capability units called gears: requirements architecture product fit framework (G1) application runtime. We are building a World Model over that pipeline a model that does not just generate code but predicts the consequences of an architectural decision: total cost of ownership unintended side effects latency and failure behaviour and whether a proposed composition is even admissible.
The central object is not a digital twin of the application but its formal architectural skeleton: 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 gap. All of it is written in a domain-specific language 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 models state space and the boundary of what the model is allowed to propose.

Internship Role

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.

Key Responsibilities

• Mine open-source repositories and extract architecture dependency and interface graphs from code.
• Help build and clean (specification architecture) datasets including synthetic project generation.
• Implement and evaluate baseline models (heuristic search graph ML or LLM generation) for single pipeline steps.
• Benchmark models against shared datasets and metrics to compare approaches.
• Build evaluation harnesses and track experiments reproducibly.
• Assist with documentation and where relevant publication activities.

Required Qualifications

• Currently enrolled in a BSc/MSc in Computer Science Data Science Mathematics or similar.
• Experience with Python and ML frameworks (PyTorch scikit-learn).
• Interest in machine learning for code software engineering or formal methods.
• Good English and teamwork skills.

Desired Qualifications

• Predictive models/time-series/etc.
• LLM tooling.
• Familiarity with graph machine learning (PyTorch Geometric or DGL).
• Interest in formal methods program or static analysis or working with code structure (e.g. AST manipulation tree-sitter or repository mining).

Application Details

Please submit:

• CV
• Short motivation letter (max. 1 page) explaining your interest and relevant skills
• Transcript of records (GPA obligatory)

We look forward to your application! The review of applications will begin immediately and will continue until the position is filled.

Please note that only applications submitted through the official application portal will be considered for recognition.