Engineer decision systems that act on their own
A small team in Mannheim working inside the supply chain operations of pharmaceutical and chemical companies. We redesign how a planning decision is actually made, encode the expertise behind it, then build the agents that carry it out. The simulation engine underneath is our own.
AI Engineer (m/f/d), Junior to Senior
Join the core engineering team building the mathematical and orchestration backbone behind adago's planning agents. You build the simulation and optimization infrastructure that lets agents rehearse real industrial complexity before they touch a live decision.
Forward-Deployed Engineer (m/f/d), Junior to Senior
You deploy adago's agents into the live operations of leading pharmaceutical and chemical companies, and you do the harder work that has to happen first: turning a client's processes, systems and data into something an agent can actually reason and act on. This is a technical role, not a slide-deck one.
Principal AI Consultant (m/f/d)
You own a client engagement end to end, from scope and technical direction to the relationship with senior stakeholders up to CIO and board level. You position adago as the innovation driver in the room, not an incremental analytics vendor.
Intern / Working Student (m/f/d)
Work inside one of our Product Development teams and build real features from the start. This is production work with your name on it, reviewed and shipped like everyone else's, not a project parked on the side.
The plans built here move real medicine and real chemicals.
Nobody hands you a solved problem. You will be paired, not stranded.
By month three you will own something people depend on.
What you can hold us to
Three things about the work, then the terms. Nothing on this page that we would not repeat to you in an interview.
- 01
You work on the engine, not around it
The discrete-event simulation, the Bayesian calibration sitting on top of it and the agent orchestration around it were all written here. There is no vendor layer between you and the part that has to be right.
- 02
The domain is in the room
Pharmaceutical and chemical planning, inside regulated operations. You hear how a decision is really made from the person who currently makes it, and you are expected to argue with them when the answer does not hold.
- 03
Research is not a side project
Thesis-level work with a leading German university on self-supervised learning for supply chains, trained on data from the same engine you would be building. You sit close to it without being pulled off the product.
Most people here started as an intern or a working student. It is still the most common way into a full-time role, and several of the engineers you would be sitting next to came in exactly that way.
Maksim GusevJunior AI Engineer
Jannis HöppnerJunior AI Engineer
Adrian ScariotJunior AI Engineer
Pranjali SinghAI Engineer
Viviana UrueñaAI Engineer
Johanna WenzelSenior AI Engineer
- Working language
- English throughout. German is welcome but not required on the engineering side.
- Where you work
- Office in Mannheim, home for the rest of the week. Proper equipment for both.
- Schedule
- Flexible hours, and an onboarding built around you rather than run off a template.
- Sport and fitness
- Costs covered, without a form to justify it.
- Further education
- Funded. Courses, training and conferences, decided on the case you make for them.
- Salary
- Competitive, and tied to what you actually take on rather than to how long you have been here.
- Independence
- Bootstrapped. What gets built is not shaped by someone else's fundraising timeline.
- The team
- Regular offsites and team events. Startup pace, with the standards of a consultancy.
Judith Senger
People & Communication
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