AI Research Scientist role at JPMorgan Chase focused on developing novel techniques, tools, and frameworks for large-scale problems in financial services. The role involves formulating problems, developing algorithms and models, conducting experiments, and contributing to business applications, open-source software, and patents.

AI Research Scientist role at JPMorgan Chase focused on developing novel techniques, tools, and frameworks for large-scale problems in financial services. The role involves formulating problems, developing algorithms and models, conducting experiments, and contributing to business applications, open-source software, and patents.

Machine Learning Scientist - NLP - VP role focused on developing and deploying Generative AI solutions within JPMorgan Chase's Machine Learning Center of Excellence. The role involves research, thought partnership, cross-functional collaboration, and leading firm-wide initiatives to apply ML models across the business. Requires a PhD or MS with experience in GenAI, ML/DL toolkits, experimental design, and communication skills.

Machine Learning Scientist specializing in NLP and Generative AI (GenAI) to develop and deploy state-of-the-art models for complex business challenges within JPMorgan Chase. The role involves research, thought partnership with leaders, cross-functional collaboration for production deployment, and leading firm-wide initiatives with large-scale frameworks. Requires a PhD or MS with experience in GenAI, ML/DL toolkits, experiment design, and communication skills.

Machine Learning Scientist (VP) in the Machine Learning Center of Excellence at JPMorgan Chase, focusing on NLP and Generative AI. The role involves owning the full lifecycle of ML solutions, from research and development to production deployment, acting as a thought leader, and driving cross-functional collaboration to implement new ML methods and large-scale frameworks for business impact within the financial services industry.

JPMorgan Chase is seeking an Applied AI ML Director in London to lead a team of specialists in developing horizontal AI capabilities (APIs, libraries) for Corporate and Investment Banking. The role involves collaborating with Line of Business AI teams, mentoring engineers, and staying at the forefront of AI advancements, with a focus on domains like Documents, Email, and Speech. Requires a PhD or top commercial AI lab experience, hands-on model training/deployment, and experience leading AI teams.

AI Researcher Senior Associate at JPMorgan Chase, focused on developing novel AI/ML techniques and models for complex, large-scale problems within the financial services domain. The role involves research projects, formulating problems, developing algorithms, conducting experiments, and contributing to business applications, open-source software, and patents.

Vice President Data Scientist role in JPMorgan's Asset & Wealth Management Risk Analytics Group, focusing on developing and deploying advanced AI/ML and LLM solutions for risk management. Responsibilities include identifying use cases, leading model development (pre-training, fine-tuning, optimization), prompt engineering, quantization, evaluation, and collaborating on model serving systems. Requires strong Python, SQL, R, PyTorch/TensorFlow, AWS, and NLP/LLM experience, with frameworks like LangChain/LangGraph/AutoGen.

Research Scientist role focused on applying sophisticated machine learning methods, particularly NLP and LLMs, to real-world problems within a financial services context. The role involves developing state-of-the-art models, publishing research, and collaborating to deploy solutions into production. It emphasizes both research exploration and practical application, with a focus on post-training and potentially agentic systems.

This role focuses on applying advanced ML methods like time series analysis, reinforcement learning, causal inference, and NLP to solve real-world problems in finance. The associate will research new methods, develop state-of-the-art models, collaborate with various teams to deploy solutions, and drive firm-wide initiatives. A PhD in a quantitative discipline and hands-on experience with ML toolkits are required, with a preference for candidates with financial industry knowledge and published research.

Quantitative trading and research role focused on RMBS and structured products, developing and maintaining models. The role involves applying machine learning and generative AI to credit modeling, data processing, model calibration, performance monitoring, and delivering analytics. Responsibilities include developing financial models, conducting back-testing, performing ML analysis on large datasets, building data analysis platforms, and collaborating with stakeholders.

This role focuses on applying AI/ML and quantitative techniques to options pricing, hedging, and quoting within financial markets. It involves developing and implementing sophisticated algorithms, quantitative models, and analytical tools for trading analysis and backtesting. The position requires experience with various machine learning techniques and programming languages like Python, KDB, C++, PyTorch, and Sklearn, with a strong emphasis on quantitative research and model development in a regulated financial environment.

Quant Modeling Associate role focused on ensuring fair lending compliance for various models, including traditional, ML/AI, and Gen AI models, within JPMorgan Chase's Risk Management and Compliance division. Responsibilities include statistical analysis, bias testing, developing mitigation algorithms, research, and documentation, with a focus on adhering to Fair Lending Laws and regulations.

Quantitative trading firm seeks a VP to research, develop, and deploy mid-frequency systematic trading strategies. Role involves applying statistical modeling and machine learning to financial markets, with a focus on alpha generation, portfolio construction, risk management, and execution. Requires end-to-end ownership from ideation to live trading and performance monitoring.

The Quant Modeling Lead - Vice President role within JPMorgan Chase's Model Risk Governance and Review (MRGR) team focuses on the independent validation and risk governance of forecasting and scoring models, including those using advanced AI/ML techniques, for consumer banking applications. The role involves evaluating model conceptual soundness, performing independent testing, monitoring performance, and liaising with various internal and external stakeholders, including regulators.

Lead Software Engineer for Commercial and Investment Banking team, focusing on researching, developing, and productionizing high-performance machine learning and quantitative models, and designing scalable data processing pipelines. Requires deep ML/DL knowledge and experience with frameworks like TensorFlow/PyTorch, and ETL/real-time data processing.

This role is for a Conversation Designer Vice President within the Chase Digital Assistant team, focusing on shaping user experience for conversational AI products like chatbots and IVRs. The role involves leading the design of conversational flows, mentoring junior designers, collaborating with ML teams to refine NLU models, and driving the strategic vision for conversational AI in finance.

Lead Data Scientist for Workforce Planning at JPMorgan Chase, focusing on developing and deploying AI/ML models for forecasting, capacity planning, and scheduling. The role involves full model lifecycle management, project leadership, stakeholder collaboration, and mentoring junior team members, with an emphasis on moving towards real-time inference and decision-making.

This role is for a Conversation Designer Vice President within the Chase Digital Assistant team, focusing on shaping user experience for conversational AI products like chatbots and IVRs. The role involves leading the design of conversational flows, mentoring junior designers, collaborating with ML teams to refine NLU models, and driving the strategic vision for conversational AI in finance.

Senior Lead Software Engineer at JPMorgan Chase focused on building and enhancing technology products. The role involves providing technical guidance, developing secure production code, influencing product design, and acting as a subject matter expert. Requires experience in machine learning, NLP, training/fine-tuning LLMs, transformer architectures, and deploying AI systems, with proficiency in Python, TensorFlow, or PyTorch. Experience with scalable distributed systems, cloud-native environments, and security protocols is also necessary.

Lead Applied AI/ML role focused on developing and deploying models and analytical methods for compliance and risk management within a financial institution. Requires strong experience in Python, R, Scala, machine learning, statistical models, and specifically graph analytics and databases. The role involves data pipeline development, working with structured and unstructured data, and preparing technical documentation for governance review.

This role focuses on quantitative research within Wealth Management, developing and maintaining models for asset allocation, portfolio construction, risk management, and performance attribution. It involves applying statistical and machine learning techniques to large datasets and collaborating with portfolio managers and other stakeholders. The role requires strong Python programming skills and a background in quantitative fields.

This role focuses on applying quantitative modeling and statistical analysis to ensure fairness and compliance in line-of-business models, including traditional, ML/AI, and Gen AI models, within a regulated financial environment. The primary goal is to detect and mitigate bias to comply with Fair Lending Laws and regulations.

Develops and industrializes quantitative methodologies and predictive models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.

Develops quantitative methodologies and predictive/explanatory models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.