Stockholm / Hybrid · Summer 2027 · Apply by
Machine Learning & Data Science Intern
Train and refine models used in real property transactions, valuations, and rent negotiations. Work directly with the founders, with a path to part-time work or a master's thesis.
The role
As a Machine Learning & Data Science Intern, you work closely with the founders on the models and datasets at the core of Tegl. Our models are used by real estate companies to value portfolios, underwrite transactions, and negotiate rents. Over the summer, you will train and evaluate models for rent setting and property valuation, fuse official public registers with market observations, and use large language models (LLMs) to structure information from documents. You will also join meetings with CEOs and decision-makers across the real estate industry.
Who we are looking for
We care much more about how you reason about data, statistics, and modeling than which specific courses you have taken. You enjoy diving into messy raw data, formulating and testing hypotheses, and building models that are both accurate and interpretable. You are likely studying engineering physics, applied mathematics, machine learning, computer science, or industrial engineering, and you use modern AI tools to work effectively.
Practical setup & master's thesis
The summer program is unpaid and flexible: you choose your schedule and duration (for example, 3–6 weeks) so you still have time for a summer break. Our office is in Vasastan in Stockholm with hybrid remote flexibility.
For the right person, the goal is for the summer to lead into paid part-time work in the autumn, a master's thesis (exjobb) in machine learning and data science on our proprietary datasets, or a full-time role after graduation. If you are primarily looking for a thesis topic for autumn or spring, feel free to reach out as well.
How to apply
No cover letter needed. Apply using the form below, email lovisa@tegl.se with the subject line "ML & Data Intern – [Your Name]", or message us on LinkedIn. Include your CV or LinkedIn profile (and GitHub if available) along with a few lines about a model, analysis, or problem you have solved that you are proud of.