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 missionHi, 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 ownThe 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 workSmall 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.RequirementsDeep applied mathematics: discrete optimization, algorithm design, and the algebra underneath both, at a level where you can build a modeling abstraction that others then work inside. A doctorate is one way to get there; shipped work is another.You have modeled and shipped optimization in industry (MILP, CP, or both), with models that survived messy data, deadlines and real users.You know where methods and solvers reach their limits, CP-SAT and Gurobi included, and you can say which technique bought you what: warm starts, rolling horizon, relax-and-fix, aggregation, matheuristics, or a plain heuristic when an exact solve is the wrong tool.You have run optimization workloads where someone was waiting on the answer, not only in notebooks: cancellation, timeouts and parallel solves are problems you have already solved once.Python at production quality: tests, types, review, and a profiler before an optimizer. You have made numerical code fast and kept it correct, including where a model meets a solver: scaling, tolerances, integrality, and the moment money stops fitting in an integer.You want to understand the customer's problem and their data, not only the model.You have worked in a team, not mostly alone.German at C1 or better, and fluent English. Team life runs in German; code and docs are English.NRW-based (Cologne office), optionally Munich, Stuttgart, Berlin area or else willing to work hybrid. Open to find a way if we fit.Nice to have: sparse or tensor numerics at scale · compiler, DSL or type-system work · performance work on numerical or compiled code · solver internals · deploying and scaling solver workloads yourself · production planning, supply chain or logistics domain knowledge.You are structured and biased for action, and you have shown you play to win wherever life has put you so far.This role is not for you if you want research freedom over product deadlines, if you would rather rewrite an engine than measure it, if you want to work only inside your own abstraction, if the data work is someone else's job, or if you are waiting for the next task to be handed to you.BenefitsImpact. Your work decides how factories plan, on real industrial data, in a product people use daily, not a benchmark set. Customers measure in euros what your work changed, and they tell you.Ownership. You own the engine at the center of the product: its mathematics, its behavior in production, and where it goes next.The people next to you. The OR engineers on the models and the solve plane, and the CTO on the platform.Feedback speed. You will know where you stand. We say things out loud, we adjust, and we expect the same from you.High stakes. Competitive salary and relevant room in the equity package (VSOP) to match your contribution and your career development.The basics. Hardware of your choice · AI tooling budget · sports membership · Deutschland-Ticket.Process: a 20-minute first call with me; a technical conversation that goes deep on your modeling and engineering work; a hands-on challenge of about three hours, followed by a 45-minute walkthrough where you explain your solution and the decisions behind it; then the team, online and then in person. Both sides decide. Two to three weeks end to end, and you hear back within days after each step. Before you decide, ask us to put you on a call with someone who will tell you the good, the bad and the ugly.To apply: skip the cover letter. Tell us about a model you built that made it into production, and one modeling decision in it you would reverse today."Hustle the day, analyze at night, reinforce something outstanding." Period.
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.
Requirements
- Deep applied mathematics: discrete optimization, algorithm design, and the algebra underneath both, at a level where you can build a modeling abstraction that others then work inside. A doctorate is one way to get there; shipped work is another.
- You have modeled and shipped optimization in industry (MILP, CP, or both), with models that survived messy data, deadlines and real users.
- You know where methods and solvers reach their limits, CP-SAT and Gurobi included, and you can say which technique bought you what: warm starts, rolling horizon, relax-and-fix, aggregation, matheuristics, or a plain heuristic when an exact solve is the wrong tool.
- You have run optimization workloads where someone was waiting on the answer, not only in notebooks: cancellation, timeouts and parallel solves are problems you have already solved once.
- Python at production quality: tests, types, review, and a profiler before an optimizer. You have made numerical code fast and kept it correct, including where a model meets a solver: scaling, tolerances, integrality, and the moment money stops fitting in an integer.
- You want to understand the customer's problem and their data, not only the model.
- You have worked in a team, not mostly alone.
- German at C1 or better, and fluent English. Team life runs in German; code and docs are English.
- NRW-based (Cologne office), optionally Munich, Stuttgart, Berlin area or else willing to work hybrid. Open to find a way if we fit.
Nice to have: sparse or tensor numerics at scale · compiler, DSL or type-system work · performance work on numerical or compiled code · solver internals · deploying and scaling solver workloads yourself · production planning, supply chain or logistics domain knowledge.
You are structured and biased for action, and you have shown you play to win wherever life has put you so far.
This role is not for you if you want research freedom over product deadlines, if you would rather rewrite an engine than measure it, if you want to work only inside your own abstraction, if the data work is someone else's job, or if you are waiting for the next task to be handed to you.
Benefits
- Impact. Your work decides how factories plan, on real industrial data, in a product people use daily, not a benchmark set. Customers measure in euros what your work changed, and they tell you.
- Ownership. You own the engine at the center of the product: its mathematics, its behavior in production, and where it goes next.
- The people next to you. The OR engineers on the models and the solve plane, and the CTO on the platform.
- Feedback speed. You will know where you stand. We say things out loud, we adjust, and we expect the same from you.
- High stakes. Competitive salary and relevant room in the equity package (VSOP) to match your contribution and your career development.
- The basics. Hardware of your choice · AI tooling budget · sports membership · Deutschland-Ticket.
Process: a 20-minute first call with me; a technical conversation that goes deep on your modeling and engineering work; a hands-on challenge of about three hours, followed by a 45-minute walkthrough where you explain your solution and the decisions behind it; then the team, online and then in person. Both sides decide. Two to three weeks end to end, and you hear back within days after each step. Before you decide, ask us to put you on a call with someone who will tell you the good, the bad and the ugly.
To apply: skip the cover letter. Tell us about a model you built that made it into production, and one modeling decision in it you would reverse today.
"Hustle the day, analyze at night, reinforce something outstanding." Period.
At Circonomit, we’re building the strategic twin of organisations – a control system that understands the true impact of time, money, and resources in order to eliminate the negative effects in systems that come from hidden interdependencies.
At Circonomit, we’re building the strategic twin of organisations – a control system that understands the true impact of time, money, and resources in order to eliminate the negative effects in systems that come from hidden interdependencies.
