You take responsibility for a real problem early and stay with it until there is a verified result. You work close to customers, product, and the people who will use what we build.
You do not just learn another tool here. You work in a team that uses AI in its own work, explains decisions openly, and shares knowledge.
MeinGPT is growing out of customer work. Many processes and responsibilities are being redesigned right now. That gives you room to shape them rather than just stepping into a finished system.
MeinGPT combines secure access to leading models with assistants, training, and tools for deeper use cases. The work does not end with a demo. A result has to make sense to the people involved and work inside the existing process.
This makes the work concrete: product decisions change how people use AI every day. Adoption and training reveal where obstacles remain. Technical solutions have to account for data, permissions, and existing systems.
companies. reached through our platform, academy, workshops, and guided adoption.
users. have already worked with MeinGPT.
MeinGPT. has been developed as SelectCode GmbH's AI product.
We work with ambition, but not through distance or status. Whoever owns a topic brings in the right people early, shows work in progress, and tests it against reality.
You stay with a topic through the result. You clarify what good looks like, make decisions, and stay responsible until the result works in reality.
You learn from experienced colleagues. Strong people from product, engineering, adoption, and sales work closely together. Knowledge is shared in reviews, joint work, and direct feedback rather than being kept to one person.
You help shape the next phase. We are scaling the product and the company while redesigning processes. You can explain what should change and help build the better solution yourself.
We build for the long term. MeinGPT is owner-managed. Growth is not an end in itself. It has to preserve customer proximity, quality, and a way of working that people can sustain.
Mistakes are made visible and used. Not every experiment works in an innovative environment. Reversible attempts are allowed to fail. We talk about them, learn from them, and increase the level of care when people, data, money, or customers are affected.
We work with focus, but not stiffness. Weekends away together, carnival at the office, and spontaneous team photos are as much a part of the team as reviews and customer meetings.
You will not receive a fully specified ticket and disappear with it for two weeks. You clarify the problem with the people who know it and stay involved in the outcome.
The work starts with an open problem. Whoever takes on the topic first works with everyone involved to understand what is failing today and what a good solution would look like. Product, engineering, and customer work therefore come together early.
Then we build a first version that people can try. Agents can research, prepare code, and support testing. People decide on architecture, language, risk, and whether the proposal solves the right problem at all.
A task is not finished simply because code was merged or a draft was sent. We check whether people understand the solution, whether it works in their workflow, and what we should carry into the product, adoption work, and documentation.
Agents help with research, implementation, and tests. People clarify the problem, decide on the architecture, and review behavior, security, and maintainability.
AI supports research, first drafts, and comparing information. Claims, sources, and the specific customer context are checked before anything is used.
AI helps prepare conversations, organize knowledge, and structure possible use cases. The people inside the company provide context and decide what is actually implemented.
Responsibility stays concrete. Anyone using AI must be able to explain which sources, tests, and human checks support a result. The greater the possible consequence, the stronger the review.
Each listing explains the role, location, working hours, and application details that apply to that position.
You like the team but none of the roles match? Write to us anyway.
The exact process depends on the role. The listing tells you which conversations and work sample are involved, so you know what to expect before applying.
You send us your CV and a few details about what interests you in the role.
We discuss the role and the relevant conditions. You get space for your questions about the work and the team.
Depending on the role, we work through a realistic technical, commercial, or conceptual case together.
Both sides clarify the remaining questions and decide whether the role and the way of working fit.
Our mission and the way we work belong together. On the About page, you can learn how we embed AI in the Mittelstand, which principles guide our decisions, and how product, adoption, and customer work connect.
Learn more about MeinGPT