Algorithm Developer (m/w/x)

United States Digital Space LLC; München; Hybrid; EUR 90.000 - 150.000; Vollzeit

Benefits dieser Stelle

Equity compensation

Hybrid work Munich

Hybrid work Düsseldorf

Hybrid work Münster

Zusammenfassung

United States Digital Space LLC is seeking an optimization engineer to design and operate end-to-end models for energy planning, dispatch, and regulatory compliance. You will work with Julia (JuMP), Gurobi/HiGHS, and Python/C++, deploying on Kubernetes and collaborating with product engineers in a hybrid setup from Munich.

The role emphasizes production-grade modeling, data-driven decisions, and close client-facing explanations of schedules and trade-offs.

Qualifikationen

• Degree in mathematics, operations research, computer science, physics, energy systems engineering or a comparable STEM qualification.
• Deep familiarity with LP and MILP and the craft around it – formulation strength, relaxations, warm starts, solver behavior.
• You've taken a model from formulation to production that others depended on.

Aufgaben

• Own optimization models end-to-end, from capacity planning to quarter-hourly dispatch and real tariffs.
• Explain model choices to customers when schedules are questioned.
• Define inputs/outputs with Product Engineers and run on Kubernetes with attention to time, memory and cost.
• Improve scaling, decomposition, and data reduction without losing accuracy.
• Interface with production systems and ensure deployment parity with customer environments.

Kenntnisse

Optimization models

LP & MILP proficiency

Julia (JuMP)

Gurobi/HiGHS

Python/C++

Kubernetes

Cloud & data

Ausbildung

Degree in mathematics, operations research, computer science, physics, energy systems or related STEM

Energy systems engineering background

Tools

Gurobi

HiGHS

Python

TypeScript

Vue/ frontend

PostgreSQL

Jobbeschreibung

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.

TasksOptimization Models End-to-End

Own capacity expansion planning over multi-year horizons, dispatch at quarter-hour resolution, real tariffs, real market rules.

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.

RequirementsEducation & 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.

BenefitsShare 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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