Quantitative Researcher, Global Allocation - Vice President

BlackRock•New York, NY

6d•Hybrid

About The Position

BlackRock is a leading global asset management firm. The Global Allocation team, within the Global Fixed Income division, is seeking an AI Engineer to innovate in financial technology and investing. This team uses both fundamental and systematic approaches, combining insights across all asset classes. They believe research and innovation are critical to success and employ a multi-disciplinary approach that intersects traditional finance and economics with data and computer science. The AI Engineer will focus on computer science and mathematical models to generate alpha through the build-out of their AI ecosystem, investment research, and systematic implementation, aiming to build leading-edge AI research, algorithmic development, and systematic strategies.

Requirements

B.S./M.S./PhD. degree in Computer Science, Engineering, or a related subject area with 4+ years of proven experience, or equivalent experience.
A passion for algorithmic development and implementation, and a keen attention to detail.
Excellent knowledge of Python, Java or C/C++.
Experience building applications using LLM frameworks.
Experience with ML model training and fine-tuning.
Experience in distributed computing and the use of GPUs.
The desire to work in a team environment with investors of many different backgrounds.
Experience working in Agile development teams with excellent collaboration skills.

Responsibilities

Collaborate with portfolio managers and analysts to research and implement comprehensive AI/ML-based solutions from start to finish.
Design and develop methods, processes, and systems to consolidate and analyze unstructured and diverse data sources to generate actionable insights and investment recommendations.
Develop systematic alpha strategies for single name equity selection, asset allocation, fixed income, and cryptocurrencies.
Integrate new AI services across the platform.
Works with different datasets and run algorithms on large size data effectively and efficiently, staying up to date with all the latest cutting-edge technologies.