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

About FION European industry is losing competitiveness because electricity here is more expensive and more volatile than in the US or China. The reason: renewables fluctuate strongly, factories consume constantly. We close that gap. FION plans and installs the right battery system for an industrial site and runs it with AI in real time against tariffs and markets. The software that dimensions, controls and monitors those systems is our own, and it runs end to end: ML and optimization in the cloud, data streaming and control, and the edge device at the customer site. The result for the customer: significantly lower energy costs and measurable CO2 savings. We earn trust by delivering systems and operating them transparently. We already work with more than 20 factories across industries such as plastics, automotive and food processing, and we are backed by early-stage investors. We're looking for an engineer with deep experience in time-series forecasting and mathematical optimization to join us as an early hire. You'll work on the forecasting and optimization core of our platform: the models that forecast industrial electricity consumption and PV generation, and the optimization that decides how each battery operates and trades against tariffs and markets within physical and grid constraints. This is a startup role: you should be comfortable switching between strategic thinking and hands-on work. 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 ener