Job Description: Machine Learning Research Intern
Blockhouse's innovative approach to quantitative finance stems from our integration of sophisticated machine learning models with advanced financial strategies. We are seeking a Machine Learning Research Intern to support our mission and help set new benchmarks in financial analytics and execution.
Key Responsibilities:
Assist in Transformer-Based Model Research: Support the development and optimization of novel transformer-based models to enhance our trading algorithms and strategies.
Contribute to Reinforcement Learning (RL) Research: Help design, evaluate, and implement reinforcement learning agents that optimize our recommendation engine.
Develop and Refine NLP Models: Work on creating and improving NLP models within a Retrieval-Augmented Generation (RAG) framework to perform tasks related to TCA (Transaction Cost Analysis) and trade execution.
Model Evaluation and Explainability: Assist in evaluating model performance and providing insights into model behaviors, ensuring transparency and trust in our AI systems.
Collaboration: Work closely with senior machine learning engineers and quant researchers to integrate machine learning solutions into our trading platform.
Continuous Learning: Stay updated with the latest advancements in machine learning, and contribute to the refinement of our models and methodologies.
Ideal Candidate Profile:
Educational Background: Currently pursuing or recently completed a Bachelor's or Master’s degree in a quantitative field such as Computer Science, Machine Learning, Artificial Intelligence, or related disciplines.
Machine Learning Enthusiast: Basic understanding of machine learning techniques, with an interest in transformer models and reinforcement learning.
Programming Skills: Proficiency in Python and familiarity with machine learning libraries such as PyTorch, TensorFlow, or Hugging Face.
Analytical and Problem-Solving Skills: Strong attention to detail, with an ability to approach complex problems with a rigorous analytical mindset.
Eager to Learn: Enthusiasm for learning and applying new methodologies to solve challenging problems.
Why You Should Join Us
Innovative Environment: Lead the charge in financial innovation at a company that's at the forefront of integrating advanced machine learning techniques with traditional financial models.
Expert Team: Work alongside the brightest minds in an environment that values bold ideas and radical solutions to complex problems.
Professional Growth: Enjoy a vibrant company culture that promotes career development, continuous learning, and work-life balance.
Compensation: Receive competitive compensation in equity only, recognizing your contributions to our success.
Work Hours: This is a part-time role requiring 20-30 hours per week.
If you are passionate about leveraging advanced machine learning techniques to drive financial innovation and eager to apply your engineering skills to solve complex problems, join us. Together, we will chart the future of finance, today.
