Applied AI ML Engineer (NLP / LLM and Graphs)

JPMorgan Chase provides financial services, including strategic advice and products to corporations, governments, wealthy individuals, and institutional investors. Its Chief Data & Analytics Office supports the firm’s data and analytics work, while the Machine Learning Center of Excellence develops and shares machine learning solutions for business problems.

*Instagram и Facebook принадлежат компании Meta Platforms Inc., деятельность которой признана экстремистской и запрещена на территории РФ

• Develop state-of-the-art machine learning models for real-world problems, including NLP, speech recognition and analytics, time-series predictions, and recommendation systems
• Collaborate with Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management teams to deploy solutions into production
• Lead, mentor, and inspire a team of AI engineers, fostering excellence, innovation, and continuous learning
• Drive firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across business areas
• Research and explore machine learning methods through independent study, conferences, experimentation, and participation in the knowledge-sharing community

• PhD in a quantitative discipline such as Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science; alternatively, an MS with significant industry or research experience in the field
• Strong background in NLP, LLM, and graph analytics, with hands-on experience and solid understanding of machine learning and deep learning methods
• Hands-on experience building agentic AI or multi-agent systems in regulated or compliance-driven environments
• Recent hands-on experience training and deploying models and pipelines
• Ability to design experiments and training frameworks, and define and evaluate intrinsic and extrinsic model-performance metrics aligned with business goals, focusing on agentic systems and LLM evaluation
• Experience with big data and scalable model training
• Strong written and spoken communication skills for communicating technical concepts and results to technical and business audiences
• Scientific thinking, ability to invent, and ability to work independently and collaboratively
• Curiosity, diligence, attention to detail, and motivation to solve complex analytical problems
• Будет плюсом: strong Mathematics and Statistics background, familiarity with financial services, continuous integration models and unit test development, graph integration with LLM, Reinforcement Learning or Meta Learning, A/B experimentation, data- and metric-driven product development, cloud-native deployment in large-scale distributed environments, ability to develop and debug production-quality code, published research in Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal