Retrieval Engineer

Staff Retrieval Engineer page is loaded## Staff Retrieval Engineer remote type:
Engineering locations:
Illinois time type:
Full time posted on:
Posted Todayjob requisition :
26-0068
*** Posting Type
*** Hybrid
* ** Job Overview
*** We are seeking a Staff Retrieval Engineer to join the Retrieval Engineering group s 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.
As a Staff Engineer, you will play a key role in defining and evolving our retrieval  architecture, shaping how we index, store, and surface data across billions of legal  documents. 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.
This 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.
*** Job Description and Requirements
***** Key Responsibilities*
* • Architect, design, and optimize retrieval infrastructure at scale, including indexing pipelines, query execution frameworks, and storage layers.
• Lead 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.
• Drive adoption of retrieval best practices: query understanding, ranking models, caching, index sharding, distributed execution, and relevance evaluation.
• Build fault-tolerant ingestion and indexing pipelines leveraging event-driven and microbatch architectures.
• Collaborate with AI/ML engineers to integrate LLM-augmented retrieval, query expansion, re-ranking, and feedback loops into production search flows.
• Partner with platform teams to ensure retrieval systems are observable, performant, and cost-efficient across multi-tenant Kubernetes clusters.
• Establish benchmarking and evaluation frameworks for precision, recall, latency, and query coverage, and drive continuous improvement in retrieval quality.
• Contribute to strategic technical decisions that shape Relativity’s future search capabilities and ensure they scale with the growth of our data and customers.
• Incorporate 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
• Mentor engineers across teams, lead design reviews, and champion technical excellence in search and retrieval.
** Required Skills and Experience*
* • 8+ years of professional experience in software engineering, with significant focus on information retrieval systems at scale.
• Deep expertise in search engines and frameworks (Elasticsearch, Solr, Lucene, Vespa, Open Search, or equivalent).
• Strong knowledge of retrieval models (BM25, vector similarity, hybrid retrieval, learning-to-rank, neural reranking).
• Proven experience with distributed systems and storage, including index sharding, replication, and consistency trade-offs.
• Strong programming skills in Java, C++, C#, Python, or Go and experience with performance optimization at the system level.
• Proficiency with data processing frameworks (Spark, Flink, Kafka, Kinesis) for indexing and retrieval pipelines.
• Track key retrieval metrics such as accuracy, latency, and fallback rate.
• Experience operating retrieval systems in cloud-native environments (Azure, AWS, or GCP), including containerization (Docker, Kubernetes) and CI/CD.
** Desirable Skills and Experience*
* • Experience integrating vector databases (Pinecone, Weaviate, Milvus, FAISS, or pgvector) into production retrieval systems.
• Familiarity with large-scale machine learning for ranking: embeddings, transformers, reinforcement learning from user feedback.
•…