Remote Machine Learning Engineer

Description:

• Blockhouse is seeking a Quantitative Machine Learning Engineer to enhance financial analytics and execution.
• The position is a part-time role that allows for the application of advanced machine learning techniques in financial trading strategies.
• Key responsibilities include implementing and fine-tuning transformer-based models, designing and evaluating reinforcement learning agents, and developing LSTM networks.
• The role also involves algorithm development and backtesting, ensuring model explainability and transparency, collaborating with quantitative teams, and contributing to continuous improvement in machine learning methodologies.

Requirements:

• Candidates should have a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related quantitative field.
• Strong expertise in advanced machine learning techniques, particularly transformer-based models, reinforcement learning (e.g., PPO), and LSTM networks is required. Knowledge of MLOps is a plus.
• Proficiency in Python and familiarity with libraries such as PyTorch, TensorFlow, and Ray is essential. Experience with distributed computing and optimization frameworks is a plus.
• Candidates must possess strong analytical skills, a detail-oriented mindset, and a natural curiosity for exploring new methodologies.
• Problem-solving abilities are crucial, with a focus on tackling complex problems with innovative solutions.
• Outstanding communication skills are necessary to convey complex technical concepts effectively across multidisciplinary teams.

Benefits:

• Employees will work in an innovative environment at the forefront of financial innovation, integrating advanced machine learning techniques with traditional financial models.
• The opportunity to work alongside some of the brightest minds in the industry, fostering a culture that values bold ideas and radical solutions.
• A vibrant company culture that promotes career development, continuous learning, and work-life balance is offered.
• Competitive equity-only compensation is provided, recognizing contributions to the company's success.
• The role requires 20-30 hours per week with flexible remote working options.
• Support for international students is available, including assistance with CPT/OPT documentation and flexible international payment arrangements.