Staff Software Engineer, ML Search

11d•Remote

About The Position

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 116 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com. Location: This role is completely remote-friendly. If you happen to live close to one of our physical office locations, our doors are open for you to come into the office as often as you'd like. Team Description: The Search & Recommendation Relevance team focuses on delivering the most relevant results when users search for anything on Reddit. Our systems and algorithms operate on the world's largest corpus of human conversation, showcasing the best answers and diverse opinions from all across Reddit on any topics - whether it's recommendations for the best hiking trail, travel advice, or reviews of the next product or restaurant. To achieve this, our Search Recommendation systems need to be built for maintainability, scalability, and low latency in mind. As a Staff Software Engineer, ML Search, you’ll build backend and pipeline systems that turn models into real search experiences for 110M+ daily users, owning data flows, ranking and retrieval services, and low-latency model-serving APIs. You’ll integrate models into production through robust interfaces and DAGs, enabling fast iteration and powering discovery across the internet’s largest community platform.

Requirements

8+ years of industry experience with a focus on search and recommendation systems.
6+ years of experience in designing, building and iterating large-scale search relevance and infrastructure systems, handling end-to-end system development.
Proven track record in delivering large and complex systems with big business impacts.
Knowledge and experience working with search systems (e.g. Lucene, Solr, ElasticSearch, Opensearch etc.).
Demonstrated expertise at cross-functional collaboration - successfully shipped several large-scale projects with complex dependencies across teams.
Proficient in object-oriented programming (Python, Golang).
Experience in API design and integration with GraphQL, REST, HTTP, Thrift or gRPC.
Experience of developing applications using large-scale data stack - e.g. Kubeflow, Airflow, BigQuery, Kafka, Kubernetes, Redis etc.

Responsibilities

Own pipelines and DAGs that move data, features, embeddings, and models through the ML lifecycle
Design/maintain ranking and retrieval services that run models in real-time
Build scalable model-serving APIs, ensuring reliability, efficiency, and performance
Create reusable infrastructure that other MLEs depend on to train, deploy, and iterate on models
Ensure pipelines and systems support high scale, low latency, and operational excellence
Enable modeling with better systems, features, and deployment pathways