Senior Principal Software Engineer, Feature Store

Redis is a company that built the product powering fast applications used globally. They are seeking a Senior Principal Software Engineer to own and drive their feature store product, which is critical for managing and serving machine learning features at scale for enterprise customers.

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
• Lead V2 Implementation: Architect 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 shape the technical roadmap. Translate customer requirements and market trends into actionable engineering priorities
• Engage with Strategic Customers: Serve as a trusted technical advisor to banks, financial institutions, and enterprise customers. Lead technical discussions, understand their ML infrastructure challenges, and ensure our platform meets their needs
• Build & Mentor the Team: Recruit, coach, and develop engineers. Foster a culture of technical excellence, ownership, and continuous improvement
• Stay Hands-On: Actively participate in design reviews, code reviews, and critical implementation work. Lead by example in technical decision-making
• Drive Adoption of Modern Practices: Champion the use of AI-assisted development tools, observability best practices, and infrastructure automation to accelerate delivery

Requirements:

• 8+ years of experience in backend/infrastructure engineering, with demonstrated expertise in building large-scale distributed systems
• 3+ years in a technical leadership role—leading teams, driving architecture decisions, and mentoring engineers
• 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