{"schemaVersion":"jobsearcher.job.v1","id":"b81a9fa4e6a0bd07928f249b","url":"https://jobsearcher.com/jobs/b81a9fa4e6a0bd07928f249b","canonicalUrl":"https://jobsearcher.com/jobs/b81a9fa4e6a0bd07928f249b","title":"Staff Retrieval Engineer","description":"Posting Type\nHybrid\nJob Overview\nWe are seeking a Staff Retrieval Engineer to join the Retrieval Engineering group at\nRelativity. This role is ideal for a deeply technical leader in information retrieval who thrives on designing large-scale search systems, optimizing retrieval infrastructure, and advancing search quality and performance across our platform.\n\nAs a Staff Engineer, you will play a key role in defining and evolving our retrieval\narchitecture, shaping how we index, store, and surface data across billions of legal\ndocuments. You will build next-generation search capabilities that blend traditional IR with modern vector search and AI-driven approaches. Your impact will span multiple teams, and you’ll collaborate with architecture, product, and data science leaders to ensure our retrieval stack is scalable, resilient, and aligned with both developer and customer needs.\n\nThis is a high-impact role for someone who combines expert retrieval engineering capabilities with strategic thinking, mentorship, and a passion for building the future of intelligent search in a cloud-native environment.\nJob Description and Requirements\nKey Responsibilities\nArchitect, design, and optimize retrieval infrastructure at scale, including indexing pipelines, query execution frameworks, and storage layers.\nLead the evolution from traditional inverted-index search to hybrid retrieval systems that combine symbolic (BM25, learning-to-rank) and semantic (vector search, embeddings, RAG) approaches.\nDrive adoption of retrieval best practices: query understanding, ranking models, caching, index sharding, distributed execution, and relevance evaluation.\nBuild fault-tolerant ingestion and indexing pipelines leveraging event-driven and microbatch architectures.\nCollaborate with AI/ML engineers to integrate LLM-augmented retrieval, query expansion, re-ranking, and feedback loops into production search flows.\nPartner with platform teams to ensure retrieval systems are observable, performant, and cost-efficient across multi-tenant Kubernetes clusters.\nEstablish benchmarking and evaluation frameworks for precision, recall, latency, and query coverage, and drive continuous improvement in retrieval quality.\nContribute to strategic technical decisions that shape Relativity’s future search capabilities and ensure they scale with the growth of our data and customers.\nIncorporate knowledge graph–driven retrieval by modeling legal entities and relationships, integrating graph queries with text/vector search, and applying KG features to improve ranking and explainability\nMentor engineers across teams, lead design reviews, and champion technical excellence in search and retrieval.\nRequired Skills and Experience\n8+ years of professional experience in software engineering, with significant focus on information retrieval systems at scale.\nDeep expertise in search engines and frameworks (Elasticsearch, Solr, Lucene, Vespa, OpenSearch, or equivalent).\nStrong knowledge of retrieval models (BM25, vector similarity, hybrid retrieval, learning-to-rank, neural reranking).\nProven experience with distributed systems and storage, including index sharding, replication, and consistency trade-offs.\nStrong programming skills in Java, C++, C#, Python, or Go and experience with performance optimization at the system level.\nProficiency with data processing frameworks (Spark, Flink, Kafka, Kinesis) for indexing and retrieval pipelines.\nTrack key retrieval metrics such as accuracy, latency, and fallback rate.\nExperience operating retrieval systems in cloud-native environments (Azure, AWS, or GCP), including containerization (Docker, Kubernetes) and CI/CD.\nDesirable Skills and Experience\nExperience integrating vector databases (Pinecone, Weaviate, Milvus, FAISS, or pgvector) into production retrieval systems.\nFamiliarity with large-scale machine learning for ranking: embeddings, transformers, reinforcement learning from user feedback.\nUnderstanding of privacy, compliance, and security requirements in enterprise search.\nExperience with observability stacks (Prometheus, OpenTelemetry) applied to retrieval systems.\nExperience with knowledge graph technologies (Neo4j, JanusGraph, TigerGraph, RDF/SPARQL, GraphQL, or property graphs) and their integration into hybrid retrieval systems.\nFamiliarity with legal tech, e-discovery, or enterprise SaaS search challenges.\nWhy Join Us?\nDefine and drive the retrieval architecture and search infrastructure strategy across Relativity’s platform.\nOperate at the intersection of retrieval science, AI/ML, and large-scale distributed systems, with scope across teams and domains.\nWork in a high-trust, action-oriented environment with autonomy, purpose, and room for deep technical exploration.\nCollaborate with product, platform, and AI leaders to deliver next-generation intelligent retrieval experiences for our customers.\nJoin a stable, cloud-native organization investing heavily in platform engineering and AI-enabled search architectures.\nBenefit Highlights\nComprehensive health, dental, and vision plans\nParental leave for primary and secondary caregivers\nFlexible work arrangements\nTwo, week-long company breaks per year\nUnlimited time off\nLong-term incentive program\nTraining investment program\nRelativity is committed to competitive, fair, and equitable compensation practices.\nThis position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.\nThe expected salary range for this role is between following values:\n$174,000 and $262,000\nThe final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.\nRequired Skills:\nAlgorithms, Automation, Debugging, Distributed Systems, Performance Tuning, Problem Solving, Project Management, Software Development, System Designs, Technical Leadership","company":"Relativity","rawCompany":"relativity","city":"Bourbonnais","state":"IL","isRemote":false,"isActive":false,"createdAt":"2026-07-16T15:46:03.513Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"519290","title":"Web Search Portals and All Other Information Services","slug":"web-search-portals-and-all-other-information-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Staff Retrieval Engineer","description":"Posting Type\nHybrid\nJob Overview\nWe are seeking a Staff Retrieval Engineer to join the Retrieval Engineering group at\nRelativity. This role is ideal for a deeply technical leader in information retrieval who thrives on designing large-scale search systems, optimizing retrieval infrastructure, and advancing search quality and performance across our platform.\n\nAs a Staff Engineer, you will play a key role in defining and evolving our retrieval\narchitecture, shaping how we index, store, and surface data across billions of legal\ndocuments. You will build next-generation search capabilities that blend traditional IR with modern vector search and AI-driven approaches. Your impact will span multiple teams, and you’ll collaborate with architecture, product, and data science leaders to ensure our retrieval stack is scalable, resilient, and aligned with both developer and customer needs.\n\nThis is a high-impact role for someone who combines expert retrieval engineering capabilities with strategic thinking, mentorship, and a passion for building the future of intelligent search in a cloud-native environment.\nJob Description and Requirements\nKey Responsibilities\nArchitect, design, and optimize retrieval infrastructure at scale, including indexing pipelines, query execution frameworks, and storage layers.\nLead the evolution from traditional inverted-index search to hybrid retrieval systems that combine symbolic (BM25, learning-to-rank) and semantic (vector search, embeddings, RAG) approaches.\nDrive adoption of retrieval best practices: query understanding, ranking models, caching, index sharding, distributed execution, and relevance evaluation.\nBuild fault-tolerant ingestion and indexing pipelines leveraging event-driven and microbatch architectures.\nCollaborate with AI/ML engineers to integrate LLM-augmented retrieval, query expansion, re-ranking, and feedback loops into production search flows.\nPartner with platform teams to ensure retrieval systems are observable, performant, and cost-efficient across multi-tenant Kubernetes clusters.\nEstablish benchmarking and evaluation frameworks for precision, recall, latency, and query coverage, and drive continuous improvement in retrieval quality.\nContribute to strategic technical decisions that shape Relativity’s future search capabilities and ensure they scale with the growth of our data and customers.\nIncorporate knowledge graph–driven retrieval by modeling legal entities and relationships, integrating graph queries with text/vector search, and applying KG features to improve ranking and explainability\nMentor engineers across teams, lead design reviews, and champion technical excellence in search and retrieval.\nRequired Skills and Experience\n8+ years of professional experience in software engineering, with significant focus on information retrieval systems at scale.\nDeep expertise in search engines and frameworks (Elasticsearch, Solr, Lucene, Vespa, OpenSearch, or equivalent).\nStrong knowledge of retrieval models (BM25, vector similarity, hybrid retrieval, learning-to-rank, neural reranking).\nProven experience with distributed systems and storage, including index sharding, replication, and consistency trade-offs.\nStrong programming skills in Java, C++, C#, Python, or Go and experience with performance optimization at the system level.\nProficiency with data processing frameworks (Spark, Flink, Kafka, Kinesis) for indexing and retrieval pipelines.\nTrack key retrieval metrics such as accuracy, latency, and fallback rate.\nExperience operating retrieval systems in cloud-native environments (Azure, AWS, or GCP), including containerization (Docker, Kubernetes) and CI/CD.\nDesirable Skills and Experience\nExperience integrating vector databases (Pinecone, Weaviate, Milvus, FAISS, or pgvector) into production retrieval systems.\nFamiliarity with large-scale machine learning for ranking: embeddings, transformers, reinforcement learning from user feedback.\nUnderstanding of privacy, compliance, and security requirements in enterprise search.\nExperience with observability stacks (Prometheus, OpenTelemetry) applied to retrieval systems.\nExperience with knowledge graph technologies (Neo4j, JanusGraph, TigerGraph, RDF/SPARQL, GraphQL, or property graphs) and their integration into hybrid retrieval systems.\nFamiliarity with legal tech, e-discovery, or enterprise SaaS search challenges.\nWhy Join Us?\nDefine and drive the retrieval architecture and search infrastructure strategy across Relativity’s platform.\nOperate at the intersection of retrieval science, AI/ML, and large-scale distributed systems, with scope across teams and domains.\nWork in a high-trust, action-oriented environment with autonomy, purpose, and room for deep technical exploration.\nCollaborate with product, platform, and AI leaders to deliver next-generation intelligent retrieval experiences for our customers.\nJoin a stable, cloud-native organization investing heavily in platform engineering and AI-enabled search architectures.\nBenefit Highlights\nComprehensive health, dental, and vision plans\nParental leave for primary and secondary caregivers\nFlexible work arrangements\nTwo, week-long company breaks per year\nUnlimited time off\nLong-term incentive program\nTraining investment program\nRelativity is committed to competitive, fair, and equitable compensation practices.\nThis position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.\nThe expected salary range for this role is between following values:\n$174,000 and $262,000\nThe final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.\nRequired Skills:\nAlgorithms, Automation, Debugging, Distributed Systems, Performance Tuning, Problem Solving, Project Management, Software Development, System Designs, Technical Leadership","datePosted":"2026-07-16T15:46:03.513Z","dateModified":"2026-07-16T15:46:03.513Z","hiringOrganization":{"@type":"Organization","name":"Relativity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bourbonnais","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b81a9fa4e6a0bd07928f249b"},"url":"https://jobsearcher.com/jobs/b81a9fa4e6a0bd07928f249b"}}