The Machine Learning Center of Excellence (MLCOE) is a world-class machine learning team which continually advances state-of-the-art methods to solve a wide range of real-world financial problems using the company’s vast and unique datasets. Strategically positioned in the Chief Technology Office, our work spans across all of J.P. Morgan’s lines of business including Corporate & Investment Banking, Asset Wealth Management, Consumer & Community Banking, and through every part of the organization from front office sales and trading, to operations, technology, finance and more. With this unparalleled access to the firm, this role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the firm operates.

As a Machine Learning Scientist within the MLCOE, you will apply sophisticated machine learning methods to a wide variety of complex domains within natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. You must excel in working in a highly collaborative environment with their MLCOE mentors, business experts and technologists in order to conduct independent research and deploy solutions into production. You must have a strong passion for machine learning, solid expertise in deep learning with hands-on implementation experience, and invest independent time towards learning, researching, and experimenting with innovations in the field. 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

Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as time-series predictions, market modelling, and decision optimizations. 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

Ph.D. or in the last year of a Ph.D. program in machine learning, statistics, mathematics, computer science, economics, finance, science, engineering, or other quantitative fields Knowledge of machine learning / data science theory, techniques, and tools Scientific thinking, ability to work with literature and the ability to implement complex projects Ability to understand business problem, study literature for a solution approach, write high quality code for the chosen method, design training and experimentation to validate the algorithms and implementation, and to evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences Ability to work both independently and in highly collaborative team environments Excellent analytical, quantitative, and problem-solving skills and demonstrated research ability Curious, hardworking, detail-oriented and motivated by complex analytical problems

Preferred qualifications, capabilities, and skills

Knowledge of Financial Mathematics, Stochastic Calculus, Bayesian techniques, Statistics, State-Space models, MCMC, DSGE models, MCTS / distributed compute, NLP, accounting Knowledge and experience with Reinforcement Learning methods Knowledge of python, Tensorflow, tf-agent, Ray, RLLib, Tune, or other ML frameworks, etc. Experience with any of OOP, graph-based computation engines, large scale software development, C++/Java/CUDA, performance focused implementations, numerical algorithms, distributed computing, cloud computing, data transformation pipelines Familiarity with continuous integration models and unit test development Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal Strong passion for machine learning and habits to invest independent time towards learning, researching, and experimenting with innovations across a variety of fields.