Reinforcement Learning Engineer (Optimization & Control)

Quick Facts

• Role: Reinforcement Learning Engineer (Optimization & Control)

• Work model: 100% Remote

• Contract: B2B

• Engagement: Full-time

Description

You will develop reinforcement learning agents for an intelligent Energy Management System (EMS) that controls when energy should be consumed, stored, purchased from the grid, or sold back. The project combines reinforcement learning, mathematical optimization, and energy system simulation to optimize battery storage and energy flows while respecting technical constraints. The aim is to reduce energy costs and maximize renewable energy utilization using data on consumption, PV production, electricity prices, weather, and battery status.

Responsibilities

• Design, train, test, and improve RL agents and models

• Develop mathematical optimization models to use 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 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

Requirements

• Commercial, hands-on experience developing, training, and evaluating reinforcement learning models

• Practical experience with at least one RL algorithm (e.g., PPO, 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

• 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

• Very good knowledge of Python

• Strong practical experience with PyTorch or TensorFlow

• 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, testable Python code

• Experience with Git

• High level of independence and ownership of technical decisions

• English at B2 level or higher