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AI Researcher (Fairness) - Executive Director / Vice President / Sr. Associate

As an AI Researcher specializing in responsible AI, you will lead end-to-end research on designing AI systems with fairness, interpretability, privacy, security, safety, and robustness in mind. This role involves developing models and algorithms, conducting experiments, and building prototypes, with a focus on high-impact business applications and contributions to the broader AI community and academic conferences.

About the role

About J.P. Morgan AI Research

The goal of J.P. Morgan AI Research is to explore and advance cutting-edge research in AI, including ML as well as related fields like cryptography, to develop and discover principles of impact to J.P. Morgan's clients and businesses. The team is comprised of experts in many fields of AI, pursuing primary research in areas related to research pillars and concrete problems in financial services. They partner with internal teams to accelerate AI adoption and collaborate with leading academic faculty globally. Headquartered in New York with presence in London and the Bay Area, this team offers unique and exciting opportunities for impact within J.P. Morgan and the broader AI community.

Responsibilities

As a Research Scientist with a specialization in responsible AI, you will lead end-to-end research to understand how AI systems can be designed holistically with fairness, interpretability, privacy, security, safety, and robustness as first-class concerns alongside business performance, ensuring corporate values are reflected in practical applications. You will work on multiple research projects in collaboration with internal and external researchers and applied engineering teams. You will be integral to all aspects of the research lifecycle, including formulating problems, gathering data, generating hypotheses, developing models and algorithms, conducting experiments, synthesizing results, building prototype applications, and communicating the significance of your research. Your output will result in high-impact business applications, open source software, patents, and/or publications in AI/ML conferences and journals. You will work with senior leaders to help define, build, and transform businesses, with a long-term goal of eliminating structural inequality and systemic racism in financial services. As a member of the responsible AI research community, you will also have the opportunity to participate in top-tier academic conferences (e.g., ACM FAccT, AAAI AI Ethics and Society, ACM International Conference on AI in Finance) to broaden the impact of your contributions.

Preferred Qualifications

PhD in Computer Science (especially AI/ML) or related fields
Research publications in prominent AI/ML venues, especially in responsible AI (e.g., ACM FAccT; AAAI AI, Ethics and Society)
Strong expertise in fairness, ethics, interpretability, safety, robustness, and/or privacy in the context of AI/ML, possibly with core AI topics such as deep learning (DL), reinforcement learning (RL), planning, information representation and retrieval, graphs, multiagent systems (MAS), and natural language processing (NLP)
Deep understanding of responsible AI topics such as formal verification techniques for machine learning models; statistical measures of fairness and their relationship with legal, philosophical and sociotechnical concerns; differential privacy; robust machine learning; causal inference; moral ambiguity; strategic classification; and counterfactual analysis
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

Minimum Requirements

Masters degree or PhD with relevant work experience in Computer Science, Statistics, Engineering or related fields
Extensive programming skills in Python, Julia, Java, C++, or similar programming languages used in production machine learning and data science
Proficient understanding of fundamental AI and ML techniques (e.g., A*, regularization) as well as fairness concepts such as fairness-performance trade-offs and statistical metrics of fairness
Practical data science experience with real world data

Professional Skills

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 solving problems related to fairness, ethics, interpretability, safety, robustness, and/or privacy in financial services (specific past experience in finance is not required)