Software Engineer

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., * 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

• 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.

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-DB1 #LI-Remote

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$230,000-$322,000 USD

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 121 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.