JPMorgan Chase
Lead Software Engineer - Machine Learning
$157K–$215K
Jersey City, NJ +1 · Full-time · Posted Jul 18, 2026
- Location
- Jersey City, NJ
New York, NY - Experience
- 5+ years applied experience.
- Education
- Formal training or certification on software engineering concepts; experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
About the job
Leads software engineering for Payments Technology, including deploying end-to-end machine-learning models and building secure, cloud-native services.
What you'll do
- Provide technical guidance and influence product and engineering decisions.
- Deploy and serve end-to-end ML models, and deploy and maintain cloud-native services.
- Develop, review, and debug production code; promote validated, responsible use of AI-assisted engineering tools.
What they ask for
- Formal training or certification in software engineering concepts and 5+ years of applied experience.
- Practical experience in system design, application development, testing, and operational stability; advanced in one or more programming languages.
- Advanced knowledge of software applications and technical processes in one or more technical disciplines; practical cloud-native experience.
- Experience leading approved AI-assisted software development and coaching engineers on safe, compliant use; experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Nice to have
- Python for traditional ML models and APIs; experience with supervised and unsupervised ML models.
- OpenAPI, Google ADK, LLamaIndex, OAuth2, OpenTelemetry, microservices, and AWS.
Pay
USD 156,750.00-$215,000.00 base salary (Jersey City, NJ); USD 156,750.00-$215,000.00 base salary (New York, NY)
Tools
Python, OpenAPI, Google ADK, LLamaIndex, Oauth2, OpenTelemetry, AWS
AI focus
Deploy and serve end-to-end ML models; lead responsible adoption of approved AI-assisted engineering tools, including validation for correctness, performance, and security; preferred experience building agentic workloads and ML models.
Model-written summary · the original posting is authoritative