Machine Learning Engineer
We are seeking a Machine Learning Engineer to join a high-impact, data-driven team focused on developing and deploying machine learning models that drive investment research and trading strategies. This role sits at the intersection of applied machine learning, large-scale data processing, and quantitative modeling.
Responsibilities
- Design, implement, and optimize scalable machine learning pipelines to process and extract insights from massive, high-frequency datasets.
- Collaborate closely with quantitative researchers and engineers to integrate ML models into production trading systems.
- Build robust tools for feature engineering, data transformation, and model evaluation tailored to financial time series and alternative datasets.
- Perform rigorous analysis to validate models and detect potential edge cases or data issues in a fast-paced, high-stakes environment.
- Leverage modern ML techniques (e.g., supervised/unsupervised learning, ensemble methods, online learning) to support predictive modeling and alpha generation.
Key Qualifications
- 2+ years of hands-on experience in applied machine learning, preferably in a production-oriented or research-heavy setting.
- Strong background in mathematics and statistics, with a focus on areas such as optimization, probability theory, and statistical inference.
- Expert-level programming skills in Python, including popular ML and data libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow).
- Proficiency in working in Linux-based environments, with solid experience handling large-scale distributed data and compute workflows.
- Working knowledge of C++ is a plus, especially for interfacing with performance-critical systems.
- Experience with large datasets, including data cleaning, transformation, and feature extraction at scale (e.g., billions of rows, TB-level data).
Bonus Skills
- Exposure to high-frequency or low-latency systems and/or time-series modeling.
- Familiarity with version control, CI/CD workflows, and modern software engineering best practices.
- Interest or experience in financial markets, algorithmic trading, or quantitative investing.
Ideal Candidate Profile
- A creative and curious mindset with the ability to translate abstract problems into practical ML solutions.
- Strong communication skills and a team-oriented approach to working with researchers and engineers.
- Proven track record of delivering production-quality ML systems in a dynamic, data-intensive environment.
