AI Specialist - Equity Derivatives

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.

Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
 

Job Description:

We are seeking an exceptional Quantitative Researcher to help expand our use of advanced machine learning across the platform. This role offers the opportunity to apply cutting‑edge AI expertise to complex and intellectually challenging financial problems within a high‑performing front‑office environment. You will contribute to innovative initiatives such as integrating LLMs into RFQ workflows, enhancing volatility‑surface modelling with autoencoders, predicting option flow with transformer architectures, and improving hybrid AI–quant calibration techniques. This is a chance to shape the next generation of quantitative tooling and directly influence trading, risk, and client outcomes across the business.

The Team:

The Equity Derivatives Quant team is a global, front‑office group with hubs in London, Paris, Hong Kong, and New York, working directly with trading, structuring, risk management, and technology to drive responsible growth across the business. You will be joining a dynamic and fast‑growing team that plays a central role in delivering innovative quant solutions to support our global equity derivatives franchise. Our commitment to excellence has been recognised externally, with Bank of America awarded IFR Equity Derivatives House of the Year in 2025 and Risk.net Equity Derivatives House of the Year in 2024. This is an opportunity to contribute to a high‑performing team operating at the forefront of the industry.

Responsibilities:

• Develop and implement advanced machine‑learning models to enhance pricing, calibration, and risk workflows across equity derivatives.

• Integrate LLMs into front‑office processes, including RFQ handling and automated trade‑entry validation against term sheets.

• Design hybrid AI–quant approaches where ML techniques accelerate or approximate calibration later refined by classical methods.

• Ensure model integrity, documentation, and control standards in alignment with internal governance and regulatory requirements.

• Support continuous enhancement of the platform, working with technology teams to industrialise and scale ML‑driven solutions.

What We're Looking For:

• Strong programming expertise in Python for research prototyping and production implementation.

• Practical knowledge of large language models (LLMs) and their application to financial or technical workflows.

• Experience building and training models for sequence modelling and prediction tasks.

• Proficiency with PyTorch for developing, training, and deploying deep‑learning models.

• Familiarity with LangChain, Retrieval‑Augmented Generation (RAG) workflows, and integrating LLMs with structured data sources.

• Ability to collaborate with other teams to deliver robust, production‑ready solutions in a fast paced environment.

Skills:

• An understanding of derivatives theory, financial mathematics, and options pricing models.

• Hands‑on experience with denoising autoencoders, vector‑quantized autoencoders (VQ‑VAEs), and related representation‑learning techniques.

• C++ experience not required but helpful