About the company
We're Redis. We built the product that runs the fast apps our world runs on. Our feature store enables organizations to manage, serve, and monitor features at scale—delivering the foundation that banks, financial services firms, and enterprise customers depend on to power mission-critical machine learning workloads.
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.
- 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.
Requirements
- 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
Conditions
- High-Impact Role: Own a critical product line and directly influence how the world’s leading organizations build ML systems
- Enterprise Customers: Work with banks, financial institutions, and Fortune 500 companies solving real, complex problems
- Technical Depth: Tackle challenges in distributed systems, low-latency serving, and ML infrastructure at scale
- Competitive Compensation: Salary, equity, and comprehensive benefits
- Flexible Work: Remote-friendly culture with flexibility to do your best work