JPMorgan Chase
Director of Software Engineering - Content and Experimentation
$204K–$285K
Columbus, OH +3 · Director · Full-time · Posted Aug 6, 2026
- Location
- Columbus, OH
New York, NY
Plano, TX
Wilmington, DE - Experience
- 10+ years applied experience and 5+ years leading technologists.
- Education
- Formal training or certification on software engineering concepts; expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field.
About the job
Leads engineering strategy and delivery for a cloud-based content and experimentation platform, including scalable content delivery, personalization, and governed experimentation.
What you'll do
- Leads technology and process implementations, engineering strategy, and delivery across teams and business areas.
- Sets governance for agentic AI-enabled engineering and SDLC/TLM automation, with controls for validation, security, resiliency, traceability, and reuse.
- Partners with Product, Design, Data/Analytics, and Marketing to define platform roadmaps, operating targets, and experimentation standards.
What they ask for
- Formal training or certification in software engineering concepts and 10+ years of applied experience; 5+ years leading technologists.
- Experience leading cross-functional technology teams and hiring, developing, and recognizing talent.
- Experience leading adoption of agentic AI-enabled engineering practices, including validation, quality gates, outcome measurement, and secure handling of sensitive inputs and outputs.
- Practical cloud-native experience; strong AWS knowledge; hands-on leadership of content delivery and experimentation capabilities in a multi-team environment.
Nice to have
- Experience with third parties, software platform operations, or coaching talent and managers.
- Experience with CMS and enterprise content lifecycle concerns.
Pay
$204,250.00-$285,000.00 USD base salary
Tools
AWS, CMS, feature flags, A/B testing frameworks, API-first architectures, event-driven architectures, SDLC/TLM
AI focus
Sets governance and direction for agentic AI-enabled engineering and SDLC/TLM automation, including adoption of enterprise-authorized tools, human-in-the-loop validation, quality gates, outcome measurement, and responsible handling of sensitive data.
Model-written summary · the original posting is authoritative