Circonomit

General

• We are building the world's decision infrastructure: the strategic twin of every industrial organization for complex combinatorial problems. We unlocked what wasn't possible before: mapping reality with its levers and constraints into a computer, then running n-dimensional optimization on critical value-chain decisions. We help Europe stay strong and the German Mittelstand make good decisions between market shifts, orders, machines and people.
• Founded by Dana (CEO) and Erik (CTO) from RWTH research, backed by a €2.8M round led by Vorwerk Ventures, with customers live on our optimization models.
• We move fast. We care. No patience for problems left unsolved.
• **Your mission**
• Hi, I'm Erik, CTO of Circonomit. This ad is specific on purpose: you should be able to tell from it whether this is your job.
• We build decision infrastructure for industrial companies: our customers model their production, with its capacities, costs and constraints, and we compute the answer to "what should we do?" before the decision is made. Our engine turns that model into one artifact that both evaluates like a spreadsheet and optimizes like a solver. It sits between two worlds: the mathematics that makes the answer correct, and the product that has to make it usable by people who are not mathematicians.
• The mathematics half of that bridge is yours; our engineers own the other. You also model real customer problems on it, because that is how you learn what the engine has to provide next.
• We will not sugarcoat it: combinatorial search is unpredictable, customer data arrives messy, and some weeks a deadline sets the priority.
• **What you'll own**
• - **The mathematics behind the models.** A model means exactly one thing, and it still means that after it reaches a solver. The algebra underneath is yours, and so is the call on what the engine should be able to express next and what it should refuse.
• - **Customer models, end to end.** Turn a planning problem, with its capacities, costs, lead times and shift plans, into a model whose answer a plant manager acts on. That includes the data it runs on: ERP and Excel exports, and catching the numbers that cannot be right before the customer does.
• - **Answers people can act on.** A planner watches the number improve, can defend it in a meeting weeks later, and still gets something usable when the honest answer is "impossible": which rules collide, and what it would cost to bend one.
• - **Scale in both directions.** A model that answers for one site still answers when it covers twelve, over more periods, against harder constraints. And a hundred customers solving at once, none of them noticing each other. How you get there is your call.
• - **Your features from first line to production.** Nobody hands you a ticket and waits.
• **How we work**
• Small team, short lines of communication, no layers. You own your work end to end: you build it, you ship it to production yourself, you run it.
• Feedback runs both ways and continuously, in daily work and in weekly one-on-ones. We talk as equals, communicate proactively, and flag it early when something isn't working out. Saying no is part of the job.
• We review each other's work, and we like being together in the Cologne office, because the fastest conversations still happen in a room. Mathematics, engineering and customer work sit in the same person here by design.

Applied Mathematician

Research Engineer

• Customer models, end to end. Turn a planning problem, with its capacities, costs, lead times and shift plans, into a model whose answer a plant manager acts on. That includes the data it runs on: ERP and Excel exports, and catching the numbers that cannot be right before the customer does.
• The interface between software and mathematics. Build it, improve it, keep it running. Where the abstraction belongs and what the modeling vocabulary has to cover, so that our engineers, and eventually our customers, can build and extend models without us.
• Answers people can act on. A solve is observable while it runs, reproducible weeks after it finished, and honest when it finds nothing: it names the rules that conflict and what relaxing them would cost. Customers plan on these results and come back asking why.
• Scale in both directions. One model growing to more sites, more periods and harder search, with whatever gets you there: a warm start, a rolling horizon, a matheuristic when an exact solve won't finish. And many customers solving at once, with the isolation that requires.
• Your features from first line to production. Nobody hands you a ticket and waits.

Applied Mathematician

Applied Operations Research Engineer