Senior Staff Machine Learning Systems Engineer, Feed Relevance
Reddit, Inc. - Remote - United States
Posted May 8, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
Senior Staff Machine Learning Systems Engineer, Feed Relevance Remote - United States Reddit is a community of communities. It's built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet's largest sources of information. For more information, visit www.redditinc.com . The Feed Relevance team is responsible for the end-to-end systems that power personalization and ranking for the main Reddit feeds. We prioritize building scalable, extensible, and highly performant personalization systems to improve the user experience and drive key business metrics. 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
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