AI / ML Engineer, Systematic Equities

Key Responsibilities:

• Machine Learning Research and Development: Develop predictive models using forecasting, ranking and classification techniques. Evaluate suitable statistical, ensemble and deep-learning methods for systematic investment applications.
• Artificial Intelligence Applications: Build and evaluate AI research tools using natural language processing, large language models, embeddings, retrieval-augmented generation and document intelligence. Extract relevant information from company filings, earnings transcripts, news and other datasets.
• Feature Engineering and Model Evaluation: Develop and select features, design experiments and assess performance on unseen data. Address data leakage, overfitting, model stability and changing data behaviour.
• Data and Research Infrastructure: Develop data collection and processing pipelines, reusable Python components, experiment tracking and reproducible research workflows.
• Implementation and Monitoring: Support the transition of research into paper trading and production workflows, including model deployment, monitoring and maintenance.
• Collaboration and Communication: Work closely with fund managers to translate investment questions into technical research. Communicate findings, model behaviour and limitations clearly through analysis and visualization.

Technical Skills:

• Strong expertise in applied machine learning, statistics, feature engineering, model selection, hyperparameter tuning and rigorous model evaluation.
• Proficiency in Python, Pandas, NumPy, scikit-learn and SQL; relevant experience with PyTorch, TensorFlow, gradient-boosting libraries or comparable frameworks.
• Hands-on experience building and evaluating AI applications using NLP, LLMs, embeddings, retrieval-augmented generation or other modern AI techniques, with attention to accuracy, reliability, latency and cost.
• Demonstrated depth in one or more areas such as time-series forecasting, ranking, tabular ML, deep learning or NLP. Expertise in every technique is not required.
• Experience with APIs, Git, reproducible workflows and the deployment or operational use of models. MLOps, cloud platforms, data engineering and agentic AI experience are additional advantages.

Experience and Qualifications:

• Minimum 3 years of relevant experience in applied ML, AI engineering or data science, with substantial hands-on ownership of complex projects and demonstrated outcomes. Candidates from outside financial services are encouraged to apply.
• Bachelor's, master's or PhD in Computer Science, Statistics, Mathematics, Engineering, Data Science or a related quantitative discipline. Strong practical capability and project delivery will be given significant weight.
• Strong analytical and problem-solving skills, the ability to work independently, and clear communication with technical and non-technical colleagues.
• A clear interest in transitioning into quantitative investing as a long-term career, with a willingness to learn investment concepts and work closely with experienced investment professionals.