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 modeling.

Benefits

Share in the Success: Competitive base salary plus equity – we set your level during the process, not based on your CV.

Top-Tier Venture Capital Network: Direct access to the Point 9 and Earlybird networks – including formats for sparring, mentoring and further education.

Unforgettable Team Offsites (Workation): Once a year, the whole team goes on a workation. Including exclusive exchanges with our Visionary Voices (e.g. Robert Habeck, Kitty Mayo, James Vincent).

Corporate Health: Full flexibility for your fitness and health with Urban Sports Club.

Out to the Plant: Seeing the boiler you modeled changes how you build the product.

Our stack: Julia with JuMP on Gurobi and HiGHS, forecasting in Python, the product in TypeScript (Vue, Node, PostgreSQL), Google Cloud with Kubernetes for optimization jobs, GitLab CI, agents on LangGraph.

Small team, no layers between you and the decision. A model change that lands on Tuesday is in front of the customer on Thursday. In-person onboarding, then hybrid from Munich, Düsseldorf or Münster.

Not a research position without deployment – and not a place where solve time is someone else's problem.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation