J.P. Morgan's Applied Innovation of AI (AI2) team is seeking a Machine Learning Scientist to tackle business-critical priorities using innovative machine learning techniques and technologies, focusing on software, cybersecurity, and technology infrastructure. The role aims to develop and deploy machine learning solutions to transform how the bank operates by addressing complex challenges in these areas.
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)
- Scientific thinking and the ability to invent
- Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
- Experience with big data and scalable model training
- Ability to develop and debug production-quality code
Responsibilities
- 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
- Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation
- 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.
Other
- Must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production.
- Must also have a passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field.
- Possess strong analytical thinking, a deep desire to learn and be highly motivated.
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking and detail-oriented, and motivated by complex analytical problems
- Ability to work both independently and in highly collaborative team environments
