2025 Machine Learning Center of Excellence Summer Associate - NLP, Speech Recognition, Quant AI, and

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 2025 Machine Learning Center of Excellence Summer Associate - NLP, Speech Recognition, Quant AI, and Time Series, successful candidate 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.

The candidate must excel in working in a highly collaborative environment with their MLCOE mentors, business experts and technologists in order to conduct independent research and help deliver solutions to the business.

The candidate 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.

Job responsibilities


* 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,


* 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


* Curious, hardworking, detail-oriented and motivated by complex analytical problems


* Ability to work both inde...