Machine Learning Engineer
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Job Description
Blockhouse's success stems from our groundbreaking approach to quantitative finance, blending sophisticated machine learning models with robust financial strategies to redefine market dynamics. We are on the hunt for a Quantitative Machine Learning Engineer to elevate our capabilities and set new standards in financial analytics and execution.
Position Overview:
We are 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:
At Blockhouse, we value talent from across the globe and are committed to supporting international students in their career growth. As an e-verified company, we can assist with CPT/OPT documentation. If your CPT/OPT deadline has passed, we are open to flexible international payment arrangements to accommodate your needs and ensure a smooth onboarding process.
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.
Blockhouse's success stems from our groundbreaking approach to quantitative finance, blending sophisticated machine learning models with robust financial strategies to redefine market dynamics. We are on the hunt for a Quantitative Machine Learning Engineer to elevate our capabilities and set new standards in financial analytics and execution.
Position Overview:
We are 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.
- 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.
- 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.
- Compensation: Receive competitive equity-only compensation, recognizing your contributions to our success.
- Work Hours: This is a part-time role requiring 20-30 hours per week, with flexible remote working options.
At Blockhouse, we value talent from across the globe and are committed to supporting international students in their career growth. As an e-verified company, we can assist with CPT/OPT documentation. If your CPT/OPT deadline has passed, we are open to flexible international payment arrangements to accommodate your needs and ensure a smooth onboarding process.
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.
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