JPMorgan Chase has 448 active AI-related job listings. The majority of these roles, 55%, are focused on agents, with application roles making up another 17%. The dominant function for these positions is Engineering, with a significant number of roles also in Product. The company is primarily hiring in the United States, followed by the United Kingdom and India. Frequent tech tags include agent_orchestration, llm_observability, and model_serving. In the last 30 days, JPMorgan Chase posted 275 new AI roles, representing a 76% increase compared to the previous 30-day period.
Currently tracking 305 active AI roles, with 1181 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $153k–$285k (avg $226k).
JPMorgan Chase currently has 445 active AI-related roles in our index. The most common open titles are: Data Scientist [Multiple Positions Available] (7), Lead Software Engineer (6), Applied AI ML-Vice President (4), Applied AI/ML Lead (4), Applied AI ML-Senior Associate (3). Most positions are in Engineering and Product.
JPMorgan Chase's active AI hiring is concentrated in: agents (57%), application (17%), serving infrastructure (9%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
JPMorgan Chase is hiring AI talent in: United States (299 roles), United Kingdom (64 roles), India (56 roles), Singapore (16 roles).
Job postings at JPMorgan Chase most frequently mention: Machine Learning, Agentic Systems, Statistics, Data Science, Large Language Models (LLMs).
In the past 30 days, JPMorgan Chase has posted 178 new AI-related roles. That is a -35% change versus the prior 30 days (274 → 178).
| Title | Stage | AI score |
|---|---|---|
| Quantitative Trading & Research - Systematic Trading - Associate Quantitative trading role focused on end-to-end alpha research and strategy deployment in equity derivatives and volatility markets, leveraging advanced data analytics, statistical modeling, and machine learning. The role involves feature engineering, building calibration/attribution/monitoring frameworks, and partnering with trading for strategy implementation, execution, hedging, and risk management. It also includes building reusable research libraries and tooling, with a plus for leveraging AI/ML and AI tooling for research acceleration and developer productivity, including AI productionization and AI agents. | AgentServe | 7 |
| Quantitative Trading & Research - Mid-Frequency Trading Strategies - Vice President / Executive Director Quantitative research role focused on developing and implementing mid-frequency trading strategies using advanced statistical modeling and machine learning techniques. The role involves the full lifecycle from ideation and research to production deployment and performance monitoring, with a strong emphasis on extracting predictive signals from complex financial datasets. | Agent | 7 |