Software Engineer II - Machine Learning

JPMorgan Chase & Co.•Wilmington, DE

About The Position

Join us as we build innovative technology that improves how customers discover and engage with products and services. You’ll work in a collaborative environment where your ideas matter, your growth is supported, and your work helps deliver meaningful outcomes for customers. As a Software Engineer II at JPMorganChase within Consumer & Community Banking within the Machine Learning Operations team, you are part of an agile team building end-to-end machine learning solutions that power customer-facing experiences. You will help solve large-scale relevance and ranking problems by engineering scalable features and contributing to machine learning operations practices that enable reliable delivery. You will partner across product and technology teams to build new systems that benefit Chase customers across lines of business.

Requirements

Formal training or certification on software engineering concepts and 2+ years applied experience
2+ years of experience building software applications or services used in production
Proficiency in at least one programming language (for example, Java or Python)
Experience with data structures, algorithms, and writing efficient, reliable code
Experience with relational databases and writing queries (for example, SQL) and familiarity with NoSQL concepts
Experience with automated testing practices (unit and integration) and collaborative code review
Ability to troubleshoot production issues using logs/metrics and structured root-cause analysis
Understanding of secure coding practices and basic security concepts (authentication, authorization, input validation)
Strong communication skills and ability to work effectively in an agile, cross-functional environment

Nice To Haves

Experience working on relevance, ranking, search, recommendations, or personalization systems
Experience engineering features and feature pipelines for machine learning use cases
Familiarity with machine learning operations practices (deployment, monitoring, model/version management)
Experience with distributed data processing or streaming frameworks
Experience with cloud-native development and continuous integration/continuous delivery practices

Responsibilities

Build and maintain software services and pipelines that support end-to-end machine learning solutions in production
Engineer scalable features and feature pipelines used in relevance and ranking models, with a focus on performance and reliability
Collaborate with machine learning, product, and engineering partners to translate customer and business needs into deliverable solutions
Implement and improve machine learning operations capabilities, including model packaging, deployment, monitoring, and automation
Develop and maintain automated tests and quality controls for data, features, and services to reduce risk and improve stability
Contribute to system observability through logging, metrics, dashboards, and alerting to support operational excellence
Troubleshoot complex issues across services and pipelines, perform root-cause analysis, and deliver sustainable fixes
Participate in code reviews and design discussions, helping raise engineering standards and maintainable patterns
Apply secure development practices and support appropriate access controls and data protection requirements
Contribute to an inclusive team culture through collaboration, shared ownership, and continuous learning