Junior Product Software Engineer
Pubblicato il 05-10-2026 - Quence – Quality in essence in Italia
TXT Quence , part of the TXT Group, is looking for a Full-Stack Developer to join the team as an intern, in a role at the intersection of customer needs and product development.
The successful candidate will work on a tool that analyzes the impact of code changes. Starting from a single change, the solution identifies which components of the system are affected and which tests actually need to be run, enabling development teams to anticipate where a change could potentially introduce malfunctions.
The solution is powered by Artificial Intelligence models that analyze software structure and dependencies, with the goal of making software more reliable and verification cycles faster and more efficient.
The intern will have a full-stack role, contributing to the development and evolution of the product while working closely with customer needs. This is an opportunity to work in a dynamic and innovative environment, gain a deep understanding of the product, and contribute directly to the development of new features.
Provide technical support to customers during the installation, configuration,
and integration of the product into their development environments;
Develop integrations, customizations, and new features based on customer needs, with the support and guidance of senior team members;
Collect and formalize customer feedback, translating it into technical requirements and specifications for the product team;
Create and maintain technical documentation and product support materials;
Collaborate with the development team in compliance with technical guidelines and quality standards;
Basic knowledge of Python, Java, TypeScript, and JavaScript;
Basic knowledge of API development and integration;
Basic knowledge of Docker and Linux;
Basic knowledge of Bash scripting;
Familiarity with Git and CI/CD tools, such as GitHub Actions or other DevOps pipelines;
Knowledge of the main Machine Learning concepts and ability to distinguish between classification and generation tasks