Engineering Manager, Search Storage

About the role

Engineering Manager, Search Storage

This role defines ownership of the search storage roadmap for Reddit communities. You will guide systems that power ranking, embeddings, and infrastructure. Decisions you make directly influence reliability for millions of users. The position operates remotely within the United States on a full-time basis. Compensation is listed between $175,000 and $230,000 USD annually.

What You Will Do

You will set direction for search storage initiatives in collaboration with product and platform stakeholders. Responsibilities include architecting scalable storage layers capable of serving both vector and lexical queries under Reddit traffic patterns. You will guide teams in building robust systems using Kubernetes, Vespa, Milvus, and Solr.

Partnership with engineers across Reddit Engineering is essential to align storage strategy with Ads and search product goals. You will mentor ICs to foster technical leadership within high-scale backend environments. Automation of lifecycle operations for databases is a core duty, covering provisioning through secure decommissioning at scale. Evaluation of open source contributions in Go, Rust, Java, and C++ will advance search infrastructure capabilities. Balancing reliability, performance, and developer workflows while meeting strict availability targets is required.

Requirements

You must bring a minimum of 3 years of experience managing high performing engineering teams in backend contexts. Your storage background needs a minimum of 7 years in infrastructure, with 5 of those years spent in high-scale environments. Experience at Reddit's scale involves understanding geo distribution, security, and self healing behaviors. You should design systems that handle full-text search, vector embeddings, and relevance ranking. Working knowledge of BM25, vector databases, and distributed systems is essential.

Day-to-Day Context

Reddit is a collection of communities built on shared interests, passion, and trust. It hosts some of the most open and authentic conversations on the internet. Each day, Reddit users submit, vote, and comment on topics they care about deeply. The platform hosts over 100,000 active communities and serves approximately 130 million daily active unique visitors. Reddit ranks among the internet's largest sources of information.

Search Infrastructure builds and operates foundational components for platform teams at Reddit. Our systems satisfy needs ranging from classical ranking and search, such as Okapi BM25, to more modern spaces involving vector embeddings. The team consists of software and machine learning engineers who build platforms and evolve technologies including Milvus, Vespa, Solr, and Kubernetes. Our platform strives to balance reliability, performance, efficiency, and developer productivity in that order. We offer abstractions that enable customers to express their needs and map them into underlying infrastructure resources such as CPU, memory, and architecture. Software engineering forms the core of our DNA. We scale the team through automation, self healing, and orchestration.

As Reddit's usage of AI and embedding models grows, conventional databases do not fit our scale. This represents an opportunity to sit at the cutting edge of this space. You will help establish the future of Reddit's search capabilities while maturing a relatively newer ecosystem of technologies. These capabilities will support everything from Reddit.com search to Ads Platforms and many other ranking or relevance use cases.

The role involves navigating challenges such as scaling search primitives to be secure, highly available, and geo-distributed. Contributing to open source ecosystems involving Go, Rust, C++, and Java is part of the contribution scope. Managing the full lifecycle of our systems, including provisioning, scaling, self healing, and decommissioning via software engineering, is a standard duty. Our interfaces are APIs and our operators are code, not humans. You will work with and abstract systems like Milvus, Vespa, and Solr to support key use cases. These include lexical search, semantic search, and relevance or ranking. Running these systems with high-efficiency without sacrificing reliability is a core expectation.