DescriptionThe 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.