AI Security Research Lead

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. As an AI Research Senior Associate in J.P. Morgan AI Research, you will work on novel techniques, tools, and frameworks to model and solve complex large-scale problems, collaborating with experts in various technical and business disciplines, contributing to high-impact business applications at the cutting edge of AI.

Work on multiple commercially-orientated research projects in collaboration with internal data scientists, applied engineering teams and business stakeholders Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments, synthesize results, gather data, build innovative solutions, and communicate research significance Contribute to high-impact business applications, open-source software, and patents Develop state-of-the art machine learning models to solve real-world problems at scale Required qualifications, capabilities, and skills PhD with relevant experience in AI security and safety landscape, particularly in the context of GenAI and agentic capabilities. Proven technical expertise (through shipped systems and/or publications in top venues) across areas such as adversarial attacks and defenses (including multimodal), training-time integrity, GenAI guardrails, safety/alignment, and safety evaluation/red teaming. Experience leading and mentoring AI researchers, partnering with engineering and business stakeholders to drive execution and strategic planning across near-term deliveries and longer-horizon research initiatives. Demonstrated ability to translate research into real-world impact—e.g., prototypes/pilots, reusable components (guardrails/eval tooling), reference architectures, or standards. Preferred qualifications, capabilities, and skills Research publications in prominent AI/ML, Software Engineering venues (e.g., conferences, journals) Practical experience with ML platforms such as TensorFlow/Keras, PyTorch Comfort with rapid prototyping and disciplined software development processes Practical software engineering experience in collaborative project settings