Key skills
Backend EngineeringInfrastructure EngineeringDistributed SystemsMachine Learning InfrastructureData PlatformsFeature Engineering SystemsPythonGoRustCloud PlatformsKafkaFlinkRedisSparkEnterprise Customer ExperienceFinancial Services DomainAIMLAWSAzureGCPLeadershipCommunication
About this role
Redis is a company that builds products for fast applications used globally. As a Senior Software Engineer for the Feature Store product, you will lead the technical vision, engage with customers, and ensure the platform meets high standards of performance and reliability.
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