JPMorgan Chase
From JPMorgan Chase's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Here, you'll grow alongside talented engineers, collaborate across disciplines, and help define what's possible.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. At JPMorganChase, we build technology that matters — solutions that empower our risk managers, protect the firm, and shape the future of financial services. Joining our Market Risk Technology team means working at the intersection of engineering excellence and real-world financial impact, where your contributions are visible, valued, and meaningful. Here, you'll grow alongside talented engineers, collaborate across disciplines, and help define what's possible.
As a Software Engineer III at JPMorganChase within the Market Risk Technology team, you will serve as a seasoned member of an agile team, designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You will be responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. Our team develops the firm's strategic calculation, time series, and scenario management platforms for Firmwide Market Risk — integrating with front-office risk management systems to give risk managers the capability to assess financial impact across adverse market scenarios at a firm-wide level.
Job responsibilities
Interface closely with business stakeholders, quantitative research analysts, and upstream and downstream technology teams across the firm to deliver well-integrated, high-quality solutions
Execute software solutions across design, development, and technical troubleshooting, thinking beyond conventional approaches to break down complex technical problems
Produce architecture and design artifacts for complex applications while ensuring design constraints are met throughout the software development lifecycle
Proactively identify hidden problems and patterns in data, using those insights to drive improvements to coding hygiene and system architecture
Contribute to software engineering communities of practice and events that explore new and emerging technologies
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Build AI-enhanced user features — including search, summarization, and explanation capabilities — with a human-in-the-loop approach to ensure correctness, security, and compliance
Foster a team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and proficient applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Professional development experience with Python, Java, or another object-oriented programming language
Solid understanding of agile methodologies, including continuous integration and delivery, application resiliency, and security practices
Demonstrated knowledge of software applications and technical processes within a technical discipline such as cloud, artificial intelligence, or machine learning
Overall knowledge of the Software Development Life Cycle
Experience communicating effectively with both business-facing customers and technical teams
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Familiarity with modern front-end technologies and UI development patterns
Familiarity with large language model application patterns and prompt/context engineering techniques
Experience working in financial services or risk technology environments
Exposure to market risk concepts such as Value at Risk, stress testing, or scenario analysis
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