Algorithm Developer (m/w/x)

Invest in Values. Produce in Freedom. Boost Europe. Europe's plants run on gas, power, heat and steam – and all of them are being rebuilt at the same time. The hard part isn't ambition, it's arithmetic: which asset to buy, when to run it, what to hedge. Europe's largest companies (~2% of total energy demand) answer these questions with our Energy OS: a digital twin of the site, an optimizer that plans it, and agents that operate it. The optimizer is yours. Twenty-year investment decisions, quarter-hourly dispatch, real tariffs, real market rules. You report directly to the CTO – and nobody stands between your model and the plant it runs on. Tasks Optimization Models End-to-End: Own capacity expansion planning over multi-year horizons, dispatch at quarter-hour resolution, and MIP production scheduling with sequence-dependent setup times. Model Physics & Contracts: Storage, thermal networks, transmission, flexibility, self-consumption, minimum part loads, start-up costs – formulate what reality actually demands. Make It Solve: Tighten big-Ms, scale models cleanly, decide where decomposition earns its complexity – and where it doesn't. Smart Data Reduction: Time series clustering, extreme periods, segmentation and aggregation – without accuracy quietly disappearing. Regulation into Constraints: Model grid fees, levies, tariffs, balancing power and intraday trading per country, and understand what they do to the objective function. Prove It's Right: Test cases on real customer systems, fixtures that catch regressions, and infeasibility output an engineer can actually diagnose. Interface & Production: Define inputs and outputs with the Product Engineers; own the runs on Kubernetes – solve time, memory and cost are your numbers. Explain to the Customer: When a customer questions a schedule, you're the one who can say why the model chose it. Requirements Education & Background: Degree in mathematics, operations research, computer science, physics, energy systems engineering, business mathematics or a comparable STEM qualification. Optimization: Deep familiarity with LP and MILP and the craft around it – formulation strength, relaxations, warm starts, solver behavior. Practice over Papers: You've taken a model from formulation to production that others depended on. Code & Solvers: Strong Julia – or strong Python/C++ and a real appetite to learn Julia (JuMP) fast. Hands-on with Gurobi or HiGHS, including reading logs and knowing when to reformulate instead of tuning. Data Sense & Engineering: Comfortable with long time series, unit and sign conventions; tests, version control, code review and CI are second nature. AI-Native: Claude Code, agents and subagents are part of your daily loop – a requirement, not a plus. High Ownership, Low Ceremony: You take on the unclear. Energy domain knowledge helps but isn't required. Nice to have: Stochastic or robust optimization, Benders or column generation, unit commitment, energy market model