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

Free resume; Berlin; Hybrid; EUR 69.000 - 77.000; Vollzeit

Benefits dieser Stelle

VSOP shares

Berlin hybrid

Remote within Germany

Zusammenfassung

FION seeks an Optimization and Forecast Engineer for Energy Systems in a hybrid Berlin setup. You will build forecasting models for industrial electricity consumption and PV generation, and develop optimization for battery dispatch under grid constraints.

You’ll work closely with the CTO and join as an early hire, shaping the platform and delivering end-to-end forecasting and optimization capabilities with real impact for factories in Germany.

Qualifikationen

• 5+ years of professional software engineering.
• Strong experience in time-series forecasting and probabilistic forecasting.
• Strong understanding of LP/MILP optimization.
• Strong Python skills with Pandas, NumPy, SciPy, scikit-learn, PyTorch.
• Experience shipping forecasting/optimization models in a commercial software setting.
• Ownership mentality to shape the platform.

Aufgaben

• Develop and improve forecasting models for time-series data and energy signals.
• Create and refine battery dispatch optimization algorithms.
• Build validation, simulation, and monitoring workflows for model quality.
• Architect production-grade code for forecasting and optimization components.
• Collaborate with software engineers to integrate models into the core platform and edge devices.

Kenntnisse

Software engineering

Time-series forecasting

Probabilistic forecasting

Mathematical optimization

Python

ML tooling

Ownership mindset

Tools

Pyomo

OR-Tools

NumPy

Pandas

SciPy

PyTorch

Cloud platforms

Jobbeschreibung

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

Job Description

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 will 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)

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)

Remote full-time en 69,000–77,000 EUR/yr

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.