Staff Search Engineer

Relativity is seeking a Staff Search Engineer to join their Retrieval Engineering group. This role focuses on designing large-scale search systems and optimizing retrieval infrastructure to enhance search quality and performance across their platform.

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

Requirements:

• 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, OpenSearch, 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
• 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
• Understanding of privacy, compliance, and security requirements in enterprise search
• Experience with observability stacks (Prometheus, OpenTelemetry) applied to retrieval systems
• Experience with knowledge graph technologies (Neo4j, JanusGraph, TigerGraph, RDF/SPARQL, GraphQL, or property graphs) and their integration into hybrid retrieval systems
• Familiarity with legal tech, e-discovery, or enterprise SaaS search challenges