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. 

As a 2025 Machine Learning Center of Excellence Summer Associate - NLP, Speech Recognition, Quant AI, and Time Series within our dynamic team, you will be given the chance to utilize advanced machine learning techniques across a range of intricate domains such as natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. 

Job responsibilities:

Create strategically 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.  Embrace opportunity to explore novel and complex challenges that could profoundly transform how the firm operates. Collaborate closely with our MLCOE mentors, business professionals, and technologists, carrying out independent research and providing solutions to the business. Demonstrate deep passion for machine learning, robust expertise in deep learning with practical implementation experience, and a dedication to learning, researching, and experimenting with innovations in the field.

Required qualifications, capabilities and skills

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. Strong background in Mathematics and Statistics. Published research in areas of natural language processing, deep learning, or reinforcement learning at a major conference or journal Expected graduation date of December 2025 through August 2026 Solid background in NLP, large language models, speech recognition and modelling, or personalization/recommendation. Familiarity with state-of-the-art practice in these domains or knowledge of Financial Mathematics, Stochastic Calculus, Bayesian techniques, Statistics, State-Space models, MCMC, DSGE models, MCTS / distributed Knowledge and experience with Reinforcement Learning methods Proficient in Python, and experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) 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 Solid written and spoken communication to effectively communicate technical concepts and results to both technical, and business audiences Ability to develop and debug production-quality code Familiarity with continuous integration models and unit test development.

Preferred qualifications, capabilities and skills

Familiarity with the financial services industries Innovative problem-solvers with a passion for developing solutions that support our global business. Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal. Curious, hardworking, detail-oriented and motivated by complex analytical problems Ability to work both independently and in highly collaborative team environments.