The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm's data and analytics journey. As a part of CDAO, The Machine Learning Center of Excellence (MLCOE) partners across the firm to shape, create, and deploy Machine Learning Solutions for our most challenging business problems. 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 intern within the MLCOE, you will apply sophisticated machine learning methods to a diverse range of complex domains, including natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. You will collaborate closely with MLCOE mentors, business experts, and technologists, conducting independent research and deploying solutions into production. A strong passion for machine learning, solid expertise in deep learning with hands-on implementation experience, and a commitment to continuous learning and innovation are essential. This role offers a unique opportunity to contribute to and learn from a world-class machine learning team. Learn more about our MLCOE team at jpmorgan.com/mlcoe.
Our Summer Associate Internship Program begins in June, depending on your academic calendar. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, an engaging speaker series with our senior leaders and more. Your project will have direct impact on JPMorgan's businesses, will be integrated into our product pipelines, or be part of published research in top AI/ML conferences. Full-time employment offers may be extended upon successful completion of the program within our hybrid work model.
Job responsibilities
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Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
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Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems
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Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Required qualifications, capabilities, and skills
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Enrolled in a PhD or MS in a quantitative discipline, e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields, or equivalent research or industry experience,
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Expected graduation date of December 2026 through August 2027
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Solid background in NLP, large language models, speech recognition and modelling, or personalization/recommendation. Familiarity with state-of-the-art practice in these domains
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Proficient in Python, and experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
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Scientific thinking, ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
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Solid written and spoken communication to effectively communicate technical concepts and results to both technical, and business audiences
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Ability to work both independently and in highly collaborative team environments
Preferred qualifications, capabilities, and skills
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Strong background in Mathematics and Statistics
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Familiarity with the financial services industries
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Published research in areas of natural language processing, deep learning, or reinforcement learning at a major conference or journal
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Ability to develop and debug production-quality code
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Familiarity with continuous integration models and unit test development
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Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal
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