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
Builds core infrastructure for AI agents to access and assemble context using Redis, shipping fast with high autonomy. Requires 5+ years backend experience, strong Go proficiency, and hands-on AI agent development.
197k – 246k/yr
Remote5+ YOEBackend Engineering
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Remote7+ YOEBackend Engineering
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Remote5+ YOEBackend Engineering
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