Optimization and Forecast Engineer - Energy Systems (f/m/d)

You'll work directly with the CTO, one of the co-founders.

Tasks

Forecasting models Develop and improve time-series forecasting models for industrial electricity consumption, PV generation, and other energy-relevant signals, and extend them with probabilistic outputs. Evaluate forecast quality on noisy, incomplete, and non-stationary industrial data, including its actual operational and economic impact.

Battery dispatch optimization Develop and improve the optimization models that schedule battery dispatch across peak shaving, self-consumption, spot market trading and flexibility marketing, while adhering to physical and grid constraints. Design how forecasts, uncertainty measures, physical constraints, and system states are used in downstream optimization workflows.

Validation, simulation and monitoring Build and improve simulation, replay, benchmarking, and validation workflows to test model behavior before and alongside deployment in live systems. Build and use tools and processes to monitor and assess the quality of operational forecasting and optimization models.

Production integration Design, write, test, and deploy production-grade code for mission-critical forecasting and optimization products. Improve robustness through plausibility checks, fallback behavior, re-forecasting, and handling of low-confidence or missing data. Collaborate closely with software engineers to integrate models into our core platform and edge devices.

Requirements

You bring:

• 5+ years of professional software engineering experience
• Strong applied experience in time-series and probabilistic forecasting: forecast calibration, uncertainty evaluation, backtesting, and model validation
• Strong understanding of mathematical optimization, especially LP/MILP, and the ability to model real-world systems through objectives, constraints, and operational rules
• Strong Python skills with the scientific and machine learning tool stack (pandas, NumPy, SciPy, scikit-learn, PyTorch)
• Experience developing, releasing, and tracking the performance of forecasting or optimization models in a commercial software setting
• An ownership mindset - you want to shape the platform, not just implement tickets

Even better if you have:

• Energy domain knowledge - you understand power, energy, phases, and how the grid works
• Understanding of European electricity markets, flexibility services, and grid operation
• BESS-specific experience: familiarity with BESS architectures, components, and operation, and direct experience with energy management algorithms for battery systems
• Experience with open-source and commercial MILP solvers (e.g. Pyomo, OR-Tools)
• Experience with optimization techniques such as stochastic or robust optimization
• Experience building forecasting and machine learning products in the cloud (e.g. AWS, GCP, Azure)
• Experience using Large Language Models to accelerate software development and reliably add new capabilities to real products
• German language skills (our customers and partners are German)

Benefits

Ownership, not tickets This isn't a role where the models are decided and you fill in the blanks. The forecasting and dispatch optimization stack is greenfield - nothing exists yet, and you'll design and build it from the ground up. At larger energy companies, this scope is split across multiple teams. Here, you own it all.

Real impact, fast Your code runs physical batteries that save real factories real money. You'll see results on a Grafana dashboard the same week you deploy.

Founding trajectory As FION grows, you grow with it - from hands-on builder to platform lead to head of forecasting and optimization. What takes 5 years at a large company, you'll have from day one.

Compensation

• VSOP (virtual shares)
• you participate in the company's success
• Berlin hybrid (remote from within Germany possible for the right person)

Send us:

• Your CV or LinkedIn profile
• A few sentences on why this role interests you
• Optionally: a link to a project, repo, or writeup that shows how you think about technical problems

We review every application personally and get back to you within a week. No automated screening, no ATS black hole - you're writing to a person, not a system.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung

Veröffentlicht am 01. Nov. 2026

Veröffentlicht am 01. Nov. 2026