AI-Machine Learning Research Engineer

About

Globally leading quantitative investment manager and an early pioneer of systematic strategies in the 1990s. Scientific and data-driven fund who have been at the forefront of computational finance for the last four decades. Multi-disciplinary team of STEM-subject matter experts who deploy a collaborative approach to developing and executing quantitative and fundamental trading strategies across an array of asset classes and investment products. Following continued stellar performance in financial markets last year, this firm are scaling their quantitative research and trading capabilities in New York. Given longstanding investments in Machine Learning, they’re in the process of creating the firm’s first central Machine Learning team, tasked with working on an array of projects across multiple technical and quantitative stakeholders.

Role Specifics

• Main objective of this role is to design, build and deploy AI solutions that drive efficiency, enhance analytical capabilities and accelerate decision-making across the firm.
• Applied AI, knowledge of AI Agents and proficiency in Agentic systems coupled with novel areas of ML are proving vital to greenfield initiatives and R&D projects for the firm.
• This role entails developing and deploying a multitude of novel ML methodologies toward greenfield AI projects, taking ownership from concept and ideation through to production and helping to shape long term AI strategy and adoption.
• Experiment with emerging AI tools and model capabilities, rapidly prototyping and integrating them across platforms to enhance usability, scalability, and effectiveness.
• Design, develop, and maintain shared AI Infrastructure and agentic applications, ensuring firmwide data integration and enhancing software development efficiency.

Required Skills

• Advanced degree in STEM or another highly computational discipline.
• Proficient knowledge of novel areas of ML including but not limited to GenAI, LLMs, DNNs, RL, CV, Agentic AI and more traditional forms like Statistical ML.
• Capable across topics in distributed systems and functional programming.
• Proficiency in one of the main OO languages: Python, C++, Java.

Desirables

• Knowledge of Agentic AI systems for Finance is very desirable.
• Strong ML-related publications in top conferences or peer-reviewed journals including ICML, NeurIPS, AISTATS and CVPR.
• Best Paper Awards or Impact accreditations across ML.
• Direct experience of translating mathematical models or data to production-level code.

If this opportunity is of interest, please apply direct or email me at asalim@hunterbond.com .