Central Liquidity Strategies (CLS) employs a range of strategies designed to optimize the firm’s trading and execution approach by providing internal liquidity solutions for portfolio managers on both a principal (risk) and agency basis.We are seeking a highly driven, results-oriented Machine Learning (ML) Python Developer to join a new and dynamic group tasked with developing our next-generation quantitative research platform. Based in Dublin, the successful candidate will have strong analytical and problem-solving skills, excellent attention to detail, and the ability to explain sophisticated technical concepts clearly and concisely.Principal ResponsibilitiesImpactful individual contributor: Contribute to a wide range of projects and deliver quickly and iteratively.Best engineering practices: Write, support, maintain, and test code following best practices, including unit testing, documentation, and automation within standard CI/CD processes.Python/ML SME: Own best practices for Python and ML frameworks used across the stack; evaluate and introduce new platforms and libraries where appropriate.ML pipeline development: Help design and implement pipelines including training, tuning at scale, and model serving and validation in a live production environment.Collaboration: Partner with team members to set the overall direction, design, and architecture of the platform; collaborate with key stakeholders across the business.Qualifications / Skills RequiredExperience: 6+ years of ML and Python experience in a quantitative finance setting, with a proven track record of deploying ML models at scale.Education: Bachelor’s degree in Mathematics, Computer Science, Financial Engineering, Operations Research, or similar.Machine learning: Hands-on experience implementing common ML/deep learning frameworks such as PyTorch and XGBoost.Technical skills: Strong Python (and optionally kdb+) for analysis and research; Git, Unix/Linux, Bash, etc.Communication: Strong communication skills and the ability to work effectively in a team environment.Nice to have: Experience with kdb+, PyKX, storage technology, cash equities, live analytics, and/or cloud tooling.
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