Senior Staff Machine Learning Systems Engineer, Feed Relevance

What You’ll Do

As a Senior Staff Machine Learning Systems Engineer, you will help define and lead the vision for the systems that power the Reddit Home Feed. While this role sits within the Feed Relevance team, it is primarily a systems architecture and backend engineering position rather than a modeling role. We are looking for a distributed systems expert to own the 'engines' of personalization—the real-time serving pipelines, low-latency candidate retrieval systems, and high-throughput data fetching layers that allow our models to impact millions of users in milliseconds.

• Architect High-Performance Retrieval: Partner with platform teams to design and implement flexible, ultra-low-latency candidate retrieval solutions that balance discovery with computational efficiency.
• Optimize the Serving Lifecycle: Design and build "contributor-friendly" serving pipelines that allow backend and product engineers to safely inject logic and features without needing deep ML expertise.
• Build Predictive Reliability: Lead the development of sophisticated shadow-testing infrastructure to evaluate distribution shifts and system performance at scale before code ever hits production.
• Drive Cross-Stack Integration: Collaborate across the stack—from GraphQL layers to upstream infrastructure—to ensure the feed’s architecture is resilient, maintainable, and extensible.
• Performance Engineering: Identify bottlenecks in the feed's ecosystem and implement systemic fixes that improve the user experience.

Qualifications

• 10+ years of experience in backend software engineering, with a focus on architecting and scaling high-throughput, low-latency distributed systems for personalization or recommendation engines
• Expert-level understanding of distributed systems, including real-time data pipelines, advanced caching strategies (e.g., Redis, Memcached), and storage layer optimization
• Proven ability to lead technical strategy across organizational boundaries, partnering with Infrastructure and Platform teams to build extensible, "contributor-friendly" architectures
• Adept at navigating complex system trade-offs, such as balancing architectural resilience and latency with the evolving needs of machine learning models
• Strong communicator and mentor who leads through collaboration and technical excellence, with experience influencing best practices across a large engineering organization