What You’ll Do

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.

What You’ll Need

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