RL Developers

We are looking for a highly skilled  Reinforcement Learning Developer to lead architecture design and hands-on implementation of RL models. The ideal candidate should have deep expertise in deep reinforcement learning (RL), the ability to guide team discussions, and experience in designing reward policies to optimize model performance. Prior experience in the trading domain is a plus.

Key Responsibilities:

• Design and implement reinforcement learning models for real-time decision-making.
• Develop and optimize reward policies to enhance model performance.
• Lead discussions on ML frameworks, learning models, and implementation strategies.
• Work with multi-agent RL systems for large-scale applications.
• Utilize GPU computing (CUDA, Tensor) for performance optimization.
• Implement and fine-tune Deep Q-Networks (DQN), PPO, and A2C for financial market simulations.
• Leverage parallel computing and distributed systems for large-scale model training.

Required Skills & Expertise:

Core Skills:

• Python: Strong proficiency in Python for ML/RL development.
• Reinforcement Learning: Expertise in DQN, PPO, and A2C.
• Machine Learning: Hands-on experience with ML logic and predictive modeling.
• Model Development: Ability to build, train, and optimize RL models.

Technical Knowledge:

• Deep Learning Frameworks: TensorFlow, PyTorch
• RL Frameworks: OpenAI Gym for training RL agents
• Data Processing: NumPy, Pandas, Scikit-learn
• Performance Optimization: Multi-agent RL, CUDA/GPU acceleration, distributed computing
• Cloud Computing: Experience with AWS, Azure, or other cloud platforms for model training

This role is ideal for a highly motivated RL expert who thrives in a fast-paced environment and is eager to build cutting-edge RL models for financial applications.