Reinforcement Learning Engineer (RL & Optimization)

Reinforcement Learning:

• Commercial, hands-on experience developing, training, and evaluating Reinforcement Learning models.
• Practical experience with at least one RL algorithm, such as PPO or DQN.
• Ability to independently design RL environments, including reward functions, state representations, action spaces, and constraints.
• Experience analyzing RL agent behavior, debugging training issues, and improving policies based on experimental results.

Mathematical optimization:

• Solid understanding of mathematical optimization fundamentals, including objective functions and constraints.
• Ability to formulate optimization problems involving discrete and continuous decisions.
• Understanding of how optimization-based solutions can be used to evaluate and benchmark RL models.

Machine Learning & Software Engineering:

• Very good knowledge of Python.
• Strong practical experience with PyTorch, TensorFlow, or another deep learning framework.
• Very good knowledge of pandas and NumPy.
• Practical experience training, testing, evaluating, and comparing Machine Learning models.
• Hands-on experience deploying Machine Learning models into production environments.
• Experience designing reliable experiments, evaluating model performance, and interpreting results.
• Ability to write clean, maintainable, and testable Python code.
• Experience with Git.
• High level of independence, problem-solving skills, and ownership of technical decisions.
• English at B2 level or higher.

Nice to have:

• Experience with Reinforcement Learning for control systems, robotics, autonomous systems, or simulation-based decision-making.
• Knowledge of energy systems, renewable energy, battery storage, or energy markets.
• Experience with Gymnasium or TorchRL.
• Experience with linear programming, MILP, or dynamic programming.
• Experience developing simulators or simulation environments for RL agents.
• Experience with time-series forecasting and time-series data processing.
• Knowledge of forecasting architectures such as TFT or TiDE.
• Experience with Optuna, DVC, or MLflow.
• Experience working with weather, renewable energy production, energy consumption, or electricity price data.
• Experience using AI coding assistants such as Claude Code or Codex.
• Experience with Docker and/or Kubernetes.

• Design, train, test, and improve Reinforcement Learning agents and models.
• Develop mathematical optimization models and use them as benchmarks for RL-based solutions.
• Define reward/objective functions, state and action spaces, decision constraints, and RL environments.
• Develop and improve simulations of battery storage systems, PV installations, and energy flows.
• Evaluate RL policies against optimization-based solutions and baseline control strategies.
• Analyze agent behavior, identify weaknesses, and improve model performance through experimentation.
• Test and validate models across different installation configurations and energy consumption profiles.
• Research and evaluate RL algorithms and optimization approaches to select suitable solutions.
• Prepare and process data related to energy consumption, PV production, weather, and electricity prices.
• Occasionally support electricity price forecasting models for the day-ahead market.