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Redis is seeking a Senior Principal Software Engineer to own and drive the technical vision of its feature store product—a critical platform serving banks, financial institutions, and enterprise customers with demanding ML infrastructure requirements.
In this role, you will be the primary technical leader for the feature store platform, blending deep technical expertise with customer-facing responsibilities. You'll own the architecture and design patterns, lead the implementation of the next-generation feature store built for scale and sub-millisecond latency, and serve as a trusted technical advisor to strategic enterprise customers in regulated industries.
Key responsibilities include defining engineering standards and code quality benchmarks, architecting systems for low-latency serving and enterprise-grade reliability, partnering with Product leadership to shape the technical roadmap, and translating customer requirements into product capabilities. You'll engage directly with major banks and Fortune 500 companies to understand their ML infrastructure challenges, lead technical discussions, and ensure the platform meets their needs.
You'll also build and mentor a world-class engineering team, recruit top talent, and foster a culture of technical excellence and ownership. The role remains hands-on—you'll actively participate in design reviews, code reviews, and critical implementation work, leading by example in technical decision-making. You'll champion modern development practices including AI-assisted tools, observability best practices, and infrastructure automation.
Required qualifications: 8+ years in backend/infrastructure engineering with demonstrated expertise in large-scale distributed systems; 3+ years in a technical leadership role leading teams and driving architecture decisions; 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); experience working with enterprise customers in regulated industries like financial services; and excellent communication skills translating complex technical concepts for both engineering teams and business stakeholders.
Nice-to-have qualifications include direct experience building or operating feature stores (Feast, Tecton, Hopsworks), real-time feature serving at sub-millisecond latencies, background in financial services or compliance-heavy environments, contributions to open-source ML infrastructure projects, and hands-on experience as a data scientist or ML practitioner.
