Reinforcement Learning Engineer (RL & Optimization)

Reinforcement Learning Engineer (Optimization & Control)

Location: 100% Remote

Contract type: B2BCapacity: Full-timeRate: 130–180 PLN + VAT / h🚀 About the projectWe are looking for a Reinforcement Learning Engineer with hands-on commercial experience in developing, training, and evaluating RL agents to join an innovative project in the energy sector.

We are developing an intelligent Energy Management System (EMS) for commercial and industrial facilities.

The system uses data on energy consumption, renewable energy production, electricity prices, and battery status to determine when energy should be consumed, stored, purchased from the grid, or sold back.

The core of the project combines Reinforcement Learning, mathematical optimization, and energy system simulation to develop intelligent control strategies for battery storage and energy flows.

The goal is to reduce energy costs and maximize renewable energy utilization while respecting the technical constraints of each installation.

Important: This is a specialized Reinforcement Learning engineering role, not a general Machine Learning, Data Science, or LLM-focused position. We are looking for someone who has already worked commercially with RL and can independently develop and improve RL-based solutions.💡 Who we areKyotu Technology is a boutique software house based in Wrocław and Warsaw, working fully remotely or in hybrid mode across Poland.

We focus on long-term, high-quality engineering and building complex, production-grade systems with real business impact. Our teams have real influence on technical and architectural decisions.🧠 Your responsibilities

Designing, training, testing, and improving Reinforcement Learning agents and models.

Developing mathematical optimization models and using them as benchmarks for RL-based solutions.

Defining reward/objective functions, state and action spaces, decision constraints, and RL environments.

Developing and improving simulations of battery storage systems, PV installations, and energy flows.

Evaluating RL policies against optimization-based solutions and baseline control strategies.

Analyzing agent behavior, identifying weaknesses, and improving model performance through experimentation.

Testing and validating models across different installation configurations and energy consumption profiles.

Researching and evaluating RL algorithms and optimization approaches to select suitable solutions.

Preparing and processing data related to energy consumption, PV production, weather, and electricity prices.

Occasionally supporting electricity price forecasting models for the day-ahead market.🧩 Must have

Reinforcement Learning:

Commercial, hands-on experience developing, training, and evaluating Reinforcement Learning models.

Practical experience with at least one RL algorithm, such as PPO, DQN, or another relevant approach.

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.📦 What we offerB2B contract130–180 PLN + VAT / h100% remote workFull-time engagement

• Long-term cooperation
• Technically challenging project combining Reinforcement Learning, mathematical optimization, simulation, and energy systems
• High level of independence and ownership
• Real influence on technical decisions, model architecture, and the direction of the solution
• Opportunity to develop intelligent control systems with a measurable impact on energy costs and renewable energy utilization
• Collaboration with experienced engineering and AI/ML specialists🚀 Interested?

If you have commercial experience with Reinforcement Learning, enjoy solving complex decision-making and optimization problems, and want to work on an impactful energy technology project, we'd love to hear from you.