Join J.P. Morgan AI Research, where you'll explore and advance cutting-edge AI research to develop impactful solutions for our clients and businesses. 

 

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 AI and machine learning to contribute to high-impact business applications and the broader AI community. Your role involves formulating problems, generating hypotheses, developing algorithms and models, conducting experiments, and communicating research significance. Your output will result in publications, high-impact business applications, open-source software, and patents.
 

Job Responsibilities:

Work on multiple research projects in collaboration with internal and external researchers and applied engineering teams.Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments, synthesize results, gather data, build prototypes, and communicate research significance.Contribute to publications in AI/ML conferences and journals, high-impact business applications, open-source software, and patents.Participate in relevant top-tier academic conferences, organize workshops, and engage with the AI research community to broaden the impact of your contributions.

 

Required Qualifications, Capabilities, and Skills:

PhD in Computer Science, Statistics, Engineering, or related fields.Programming skills in Python.Proficient understanding of fundamental AI and ML techniques (e.g., A*, regularization).Practical experience with statistical data analysis and experimental design.Curiosity, creativity, resourcefulness, and a collaborative spirit.Effective verbal and written communication skills with technical and business audiences.Demonstrated ability to work on multi-disciplinary teams with diverse backgrounds.Interest in problems related to the financial services domain

 

Preferred Qualifications, Capabilities, and Skills:

Research publications in prominent AI/ML venues (e.g., conferences, journals).Expertise in Trustworthy AI topics like explainability, safety, robustness, fairness.Expertise in developing and using multimodal foundation models.