Applied AI ML Associate - Machine Learning Scientist – Machine Learning for Technology

Join the elite Applied Innovation of AI (AI2) team at JP Morgan Chase, strategically located within the CTO office.

As a Machine Learning Specialist within the JPMC businesses, you will be responsible for addressing business-critical priorities using innovative machine learning techniques. You will work closely with stakeholders to execute projects that support the growth of the business and explore novel challenges that could revolutionize the way the bank operates. Your role will involve applying advanced machine learning methods to a range of complex tasks, such as data mining, text understanding, anomaly detection, and generative AI. You will collaborate with business, technologists, and control partners to deploy solutions into production. Additionally, your responsibilities will include researching new methods, developing models, and contributing to reusable code and components.

Job Responsibilities:

Research and explore new machine learning methods through independent study, attending conferences, and experimentation. Develop state-of-the-art machine learning models to solve real-world problems in Cybersecurity, Software, and Technology Infrastructure. Collaborate with partner teams to deploy solutions into production. Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models. Contribute to reusable code and components shared internally and externally.

Required Qualifications, Capabilities, and Skills:

PhD in a quantitative discipline (., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) or an MS with industry or research experience. Hands-on experience and solid understanding of machine learning and deep learning methods. Extensive experience with machine learning and deep learning toolkits (., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Scientific thinking and the ability to invent. Ability to design experiments and training frameworks, and evaluate metrics for model performance aligned with business goals. Experience with big data and scalable model training. Solid written and spoken communication to effectively communicate technical concepts and results. Curious, hardworking, detail-oriented, and motivated by complex analytical problems. Ability to work both independently and in collaborative team environments.

Preferred Qualifications, Capabilities, and Skills:

Experience with A/B experimentation and data/metric-driven product development. Experience with cloud-native deployment in a large-scale distributed environment. Knowledge of large language models (LLMs) and accompanying toolsets (., Langchain, Vector databases, open-source Hugging Face Models). Knowledge in Reinforcement Learning or Meta Learning. Published research in areas of Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal. Ability to develop and debug production-quality code. Familiarity with continuous integration models and unit test development.