Senior Software Engineer, Feature Store

Redis is a company that provides technology for fast applications used globally. The Senior Software Engineer for the Feature Store product will lead technical vision and execution, engage with strategic customers, and define engineering standards for the platform.

Responsibilities:

• Own Technical Excellence: Define and drive the architecture, design patterns, and engineering standards for the feature store platform. Set a high bar for code quality, system reliability, and performance
• V2 Implementation: Assist and execute the next generation of our feature store—building for scale, low-latency serving, and enterprise-grade reliability
• Guide Product Roadmap: Partner with Product and leadership to help shape the technical roadmap. Translate customer requirements and market trends into actionable engineering priorities
• Drive Adoption of Modern Practices: Champion the use of AI-assisted development tools, observability best practices, and infrastructure automation to accelerate delivery

Requirements:

• 5+ years of experience in backend/infrastructure engineering, with demonstrated expertise in building large-scale distributed systems
• Deep experience with ML infrastructure, data platforms, or feature engineering systems at scale
• Expertise in Python, Go, and Rust
• Strong knowledge of cloud platforms (AWS, GCP, Azure) and modern data infrastructure (Kafka, Flink, Redis, Spark, or similar)
• Experience working with enterprise customers, particularly in regulated industries like financial services
• Excellent communication skills—able to translate complex technical concepts for both engineering teams and business stakeholders
• Direct experience building or operating feature stores (Feast, Tecton, Hopsworks, or custom implementations)
• Experience with real-time feature serving at sub-millisecond latencies
• Background in financial services, banking technology, or compliance-heavy environments
• Contributions to open-source ML infrastructure projects
• Hands-on experience as a data scientist or ML practitioner—training and deploying models in production