Job Title:
Senior Software Engineer, Backend
Location:
Menlo Park
CA
United States
Department:
Product Development
Job Requisition Number:
0000-00
Job Summary:
We’re now looking to expand our Machine Learning capabilities and are hiring a Senior Software Engineer who brings expertise in backend systems and applied machine learning. At Quicken, you’ll be part of a collaborative, customer-driven environment where you’ll work on AI-powered features that transform the way users interact with their financial data. You will have a real-world impact building and deploying ML services at scale, while continuing to shape the future of personal finance.

Responsibilities:

• Deliver scalable, production-ready backend/server code in Java, while integrating and deploying machine learning models and services within the Quicken Cloud Services architecture
• Collaborate with data scientists and product teams to translate ML prototypes into reliable, maintainable, and scalable APIs and service.
• Architect and implement real-time and batch inference pipelines on cloud infrastructure (AWS), ensuring high performance and reliability
• Leverage data engineering and data platform best practices to support feature extraction, data versioning, and model lifecycle management
• Own the end-to-end lifecycle of ML services: model deployment, monitoring, retraining, and A/B testing in production
• Mentor other engineers and contribute to engineering excellence through code reviews, design discussions, and process improvements
• Stay current with advances in ML infrastructure, MLOps tooling, and cloud-native technologies

Qualifications:

BONUS SKILLS

• Experience with vector databases, embeddings, or retrieval-augmented generation (RAG) pipelines is a plus
• Background in financial services or fintech is a strong advantage

EDUCATION

• BS/MS in Computer Science, Engineering, or a related field. Advanced coursework in ML/AI is a plus

• Deliver scalable, production-ready backend/server code in Java, while integrating and deploying machine learning models and services within the Quicken Cloud Services architecture
• Collaborate with data scientists and product teams to translate ML prototypes into reliable, maintainable, and scalable APIs and service.
• Architect and implement real-time and batch inference pipelines on cloud infrastructure (AWS), ensuring high performance and reliability
• Leverage data engineering and data platform best practices to support feature extraction, data versioning, and model lifecycle management
• Own the end-to-end lifecycle of ML services: model deployment, monitoring, retraining, and A/B testing in production
• Mentor other engineers and contribute to engineering excellence through code reviews, design discussions, and process improvements
• Stay current with advances in ML infrastructure, MLOps tooling, and cloud-native technologie