Software Engineering

Fixed-term contract (typically 3 months) · 80–100% · Zürich · Students (Bachelor, Master or PhD level)

«Making sense of data is the winning competence in all industries.»

We build D ONE with the belief that every company will be a data company. Since our beginnings, we help our clients to make sense of their data and generate value.

Agentic coding tools have changed how software gets written, faster than the profession has worked out how to use them well. In this internship you will work independently on a topic in this area: building real software with these tools, studying what actually works, and working alongside senior D ONE consultants.

Your tasks will include:

• Building software with agentic coding tools, and paying attention to where they help and where they get in the way.
• Researching the topic across academic literature, vendor documentation and practitioner sources.
• Comparing tools and working styles on realistic engineering tasks.
• Working with senior consultants to capture their practical experience.
• Documenting your work and presenting it to a technical and a non-technical audience.

What you bring:

• Current enrolment in a Bachelor's, Master's or PhD programme in Computer Science, Data Science, Computational Linguistics, Engineering, Mathematics or a comparable field.
• Interest in at least one industry domain and curiosity about how data creates value in it.
• Down-to-earth and pragmatic results-oriented attitude.
• Experience in working with large data sets and databases.
• Knowledge in relevant technologies and programming languages (e.g. Python, SQL, Git, Spark, cloud platforms).
• Excellent communication skills in English; German is a strong plus.

Preferred qualifications:

• Practical experience using agentic coding tools such as Claude Code, Cursor, GitHub Copilot, Zed or OpenCode on your own projects.
• Care for how code is structured: readable modules, sensible abstractions, and an instinct for when something is getting too complicated.
• Awareness that performance matters, and the ability to reason about why one implementation is slower than another.
• Comfort finding your way around a codebase you did not write.
• Solid habits around testing and version control.
• A critical eye for code generated by a model, and a working understanding of large language models and tool calling.
• Enjoyment of research and writing: reading widely, separating evidence from marketing, and explaining what you found.