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RedisRedisUnited States

Senior Software Engineer, Feature Store

Leads technical vision and execution for the Feature Store platform, building scalable ML infrastructure with low-latency serving. Requires 5+ years backend experience, expertise in Python/Go/Rust, cloud platforms, and data tools for enterprise customers.

197k – 246k/yr
Remote5+ YOEBackend Engineering

About the role

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

Nice to Have

  • 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

Skills

PythonGoRustAWSGCPAzureKafkaFlinkRedisSparkFeastTectonHopsworks
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