2025 Machine Learning Center of Excellence Summer Associate - NLP and Time Series Reinforcement Learning

JPMorgan Chase & Co.

2025 Machine Learning Center of Excellence Summer Associate - NLP and Time Series Reinforcement Learning

Salary Not Specified

JPMorgan Chase & Co., City of Westminster

  • Full time
  • Temporary
  • Onsite working

Posted 1 week ago, 10 Sep | Get your application in now before you're too late!

Closing date: Closing date not specified

job Ref: 03fcbdce503e469d92cffef4fd0346ac

Full Job Description

  • 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.

    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.

    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., J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.