JP Morgan Chase's Applied Innovation of AI (AI2) team aims to solve business-critical priorities in Cybersecurity, Software, and Technology Infrastructure using innovative machine learning techniques to transform how the bank operates.
Requirements
- Hands-on experience and solid understanding of machine learning and deep learning methods
- Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
- Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine-tuning)
- Experience with big data and scalable model training
- 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
- Scientific thinking and the ability to invent
Responsibilities
- Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation
- Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
- Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
- Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
- Contribute to reusable code and components that are shared internally and also externally
- Apply sophisticated machine learning methods to a wide variety of complex tasks including data mining and exploratory data analysis and visualisation, text understanding and embedding, anomaly detection in time series and log data, large language models (LLMs) and generative AI for technology use-cases, reinforcement learning and recommendation systems
- Design experiments and training frameworks, and outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
Other
- PhD in a quantitative discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.) with 1 year experience Or Masters with 2 years of industry or research experience in the field
- Curious, hardworking and detail-oriented, and motivated by complex analytical problems
- Ability to work both independently and in highly collaborative team environments
- Ability to effectively communicate technical concepts and results to both technical and business audiences
- Passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field
