Applied Operations Research Engineer

Circonomit

Applied Operations Research Engineer developing decision infrastructure for industrial companies. Collaborating with customers to translate real problems into optimization models for production planning.

Posted 8/2/2026full-timeCologne • 🇩🇪 GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in combinatorial optimization, particularly with MILP and CP models, while ensuring production-quality Python code and effective collaboration within a team. Proficient in understanding customer problems and data, with a focus on delivering actionable insights through robust modeling techniques.

Highest-signal resume keywords

Combinatorial OptimizationProduction-Quality PythonCP-SAT and GurobiOptimization Workloads in ProductionGerman Language Proficiency (B2 or Better)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Combinatorial OptimizationMILPCPProduction-Quality PythonCP-SATGurobiWarm StartsRolling HorizonHeuristic TechniquesData Analysis

Soft Skills

Structured ApproachBias for ActionTeam CollaborationCustomer Understanding

Tools & Technologies

ERP SystemsExcelOptimization SolversProfilers

Industry Keywords

Production PlanningSupply ChainLogisticsNumerical Performance WorkDSL or Compiler Work

Tech Stack

Tools & technologies

ERPPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

You have modeled and shipped combinatorial 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, or a heuristic when an exact solve is the wrong tool.
You have run optimization workloads in production, not only in notebooks. They get cancelled, they time out, they run in parallel, and you have made that work.
Python at production quality: tests, types, review, and a profiler before an optimizer.
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 B2 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: solver internals · performance work on numerical or compiled code · DSL or compiler work · 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

Comp & perks

Impact. Your models decide 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. No layers between you and production, no approval chain that turns your decision into someone else's. What you build, you ship, and you own it once it runs.
The people next to you. The math and OR team on the engine, the CTO on the platform, and the founders on where this goes.
Feedback speed. You will know where you stand, we speak out loud, we adjust, we grow. Together.
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.