Overview
Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation, and knowledge sharing. Develop state-of-the-art machine learning models to solve real-world problems (e.g., NLP, LLMs, and recommendation systems) and produce outputs that drive high-impact business applications, open-source software, patents, and publications in top AI/ML venues. Collaborate with partner teams to deploy solutions into production and drive large-scale frameworks to accelerate ML adoption across the business, as part of the Machine Learning Center of Excellence (MCLOE).
You will work with a multi-disciplinary team focused exclusively on Machine Learning, employing cutting-edge techniques in Deep Learning, Reinforcement Learning, and related disciplines. The role requires strong collaboration with business, technologists, and control partners to deploy solutions into production and a sustained passion for learning and experimentation.
Responsibilities
- Explore and implement new machine learning methods; conduct independent study, attend conferences, and contribute to knowledge sharing.
- Develop state-of-the-art ML models to address real-world problems in NLP, LLMs, and recommendation systems.
- Produce outputs that drive business impact, open-source contributions, patents, and publications.
- Collaborate with Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management to deploy ML solutions in production.
- Lead firm-wide initiatives by building large-scale frameworks to accelerate ML deployment across business areas.
- Partner with the MCLOE team and other stakeholders to create and share ML solutions for challenging business problems.
- Work with a multidisciplinary team of ML experts and leverage advanced techniques in Deep Learning and Reinforcement Learning.
- Communicate effectively with both technical and non-technical audiences and maintain a strong motivation for independent learning and research.
Qualifications
- PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science) with reasonable industry experience, or an MS with significant industry or research experience.
- Extensive experience with ML/DL toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
- Strong ability to design experiments and training frameworks; capable of evaluating intrinsic and extrinsic metrics aligned with business goals.
- Experience with big data and scalable model training; solid written and spoken communication for technical and business audiences.
- Solid background in NLP and LLMs; strong mathematical and statistical foundation; familiarity with financial services context is a plus.
- Proven ability to work independently and in highly collaborative team environments; strong analytical thinking and motivation to learn.
- Knowledge in search/ranking, Reinforcement Learning, or Meta Learning; expertise in recommendation systems.
- Experience with A/B experimentation and data/metric-driven product development; cloud-native deployment in large-scale distributed environments; ability to develop and debug production-quality code.
- Published research in ML/DL/RL at major conferences or journals.
- Excellent written and spoken communication to convey technical concepts and results to technical and business stakeholders.
About the Company
J.P. Morgan is a global leader in financial services, providing strategic advice and products to corporations, governments, and institutions. Our corporate functions cover finance, risk, human resources, marketing, and more, supporting our businesses, clients, customers, and employees to achieve their objectives.
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