Machine Learning Engineer Intern

Position Overview

Blockhouse is seeking a highly motivated and technically proficient Machine Learning Engineer Intern to join our dynamic team. This part-time role offers a unique opportunity to apply advanced machine learning techniques, including transformers, PPO (Proximal Policy Optimization), LSTMs, and other deep reinforcement learning algorithms, to drive innovation in financial trading strategies.

Key Responsibilities

• Implement and Fine-Tune Transformer-Based Models: Develop and optimize transformer-based models to enhance our trading algorithms and strategies. Your work will directly impact our ability to predict market movements and optimize execution strategies.
• Design and Evaluate Reinforcement Learning Agents: Design, evaluate, and implement reinforcement learning agents using state-of-the-art techniques like PPO. Focus on creating agents that can learn optimal trade execution strategies from raw market data.
• Develop and Integrate LSTM Networks: Utilize LSTM networks to model and predict temporal patterns in market data, improving the accuracy and performance of our trading strategies.
• Algorithm Development and Backtesting: Develop and rigorously backtest new machine learning algorithms to ensure they provide a tangible performance boost over existing models.
• Model Explainability and Transparency: Provide detailed explanations and insights into model decisions and behaviors, ensuring transparency and trust in our AI systems.
• Collaborate with Quantitative Teams: Work closely with quantitative researchers and data scientists to integrate machine learning solutions effectively into our trading platform.
• Continuous Improvement: Stay updated with the latest developments in machine learning and quantitative finance, contributing to the continuous improvement of our models and methodologies.

Ideal Candidate Profile

• Educational Background: Bachelors, Master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related quantitative field.
• Machine Learning Expertise: Demonstrates strong expertise in advanced machine learning techniques, particularly in transformer-based models, reinforcement learning (e.g., PPO), and LSTM networks. Knowledge of MLOps is a plus.
• Programming Proficiency: Proficient in Python and familiar with relevant libraries and tools such as PyTorch, TensorFlow, and Ray. Experience with distributed computing and optimization frameworks is a plus.
• Analytical Mindset: Detail-oriented with a rigorous approach to analysis and a natural curiosity for exploring new methodologies. Strong mathematical and statistical skills are essential.
• Problem-Solving Abilities: Comfortable tackling complex problems with innovative solutions, maintaining clarity of purpose and direction.
• Communication Skills: Possesses outstanding communication skills, capable of conveying complex technical concepts and results effectively across multidisciplinary teams.

Why You Should Join Us

• Innovative Environment: Be at the forefront of financial innovation, integrating advanced machine learning techniques with traditional financial models.
• Expert Team: Work alongside some of the brightest minds in the industry, fostering a culture that values bold ideas and radical solutions.
• Professional Growth: Enjoy a vibrant company culture that promotes career development, continuous learning, and work-life balance.