Student RL Engineer

1 week geleden

Rotterdam, Zuid-Holland, Nederland SkillMix Voltijd

About SkillMix

SkillMix is a
student-founded startup offering consulting services

to companies in the form of short- and long-term projects ranging from 4 to 12 weeks. While we recruit student consultants from multidisciplinary backgrounds, our
core expertise lies in AI, optimisation, and market research.

We are expanding our energy research and are looking for
Student Reinforcement Learning (RL) Engineers

to join our trading strategy development team

Project Description

SkillMix is launching a research project to develop a
reinforcement learning-based energy trading strategy

for volatile electricity and gas markets. This role focuses on
designing and training reinforcement learning algorithms, integrating external forecasting models

such as XGBoost and Transformers into the agent, and building tools to interpret and validate the RL policy. 

Key Responsibilities

  • Design and implement an RL framework for energy trading (e.g., DQN, PPO, SAC).
  • Integrate external forecasting models (XGBoost, Transformers) into the agent's state or reward design.
  • Build or adapt a simulation environment that reflects real-world trading conditions, including position limits, transaction costs, and market rules.
  • Implement and compare baseline trading strategies against RL agents.
  • Evaluate strategies using trading-specific metrics such as Sharpe ratio, drawdown, and risk-adjusted returns.
  • Analyse the robustness of the learned policy under different volatility regimes and stress events.
  • Implement interpretability methods to understand RL agent decisions and forecast model influence.
  • Produce clean, maintainable research code and present insights to SkillMix and the client.

Requirements

  • Strong understanding of reinforcement learning (value-based and policy gradient methods).
  • Proficiency in Python and ML libraries (NumPy, pandas, scikit-learn, PyTorch or TensorFlow).
  • Experience with time series forecasting and models such as XGBoost, LightGBM, LSTM, and Transformers).
  • Understanding of evaluation metrics for trading systems (Sharpe ratio, drawdown, risk-adjusted returns).
  • Strong analytical and experimental design skills.
  • Ability to write clean, maintainable research code.
  • Student in the Netherlands, Belgium, or able to commute to Rotterdam.
  • Pursuing or completed a degree in Computer Science, Data Science, Econometrics, or a related field.

Plus Points

  • Direct experience with reinforcement learning in trading environments.
  • Experience in deep learning model training, hyperparameter tuning, or AWS (S3, Step/Lambda functions, SageMaker).
  • Familiarity with market mechanism design or auction theory in energy systems.
  • Understanding or multi-agent or hierarchical RL.
  • Knowledge of electricity or gas markets, including balancing markets and renewable intermittency.
  • Experience with simulation environments (Gymnasium, vectorised simulators).
  • Familiarity with interpretability tools (SHAP, attention analysis, feature importance).

What We Offer

  • Flexible, part-time role suitable for students.
  • A collaborative team environment working on cutting-edge RL applications in energy markets.
  • The chance to contribute to a real-world, data-driven trading research project.
  • Work alongside a diverse and collaborative student team.

Location:

Hybrid 

Start Date:

As soon as candidate is available 

Commitment:

Part-time (8-10 hours per week) / Flexible

SkillMix values diverse perspectives. We encourage all students, regardless of background, to apply.

To apply:

Please complete the application form available with this description, or use

 

Questions:

For any inquiries regarding recruitment, please contact

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