Posting Type

Hybrid

Job Overview

We are seeking a Staff Retrieval Engineer to join the Retrieval Engineering group at
Relativity. 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.

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, 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.

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.

• 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.

Why Join Us?

• Define and drive the retrieval architecture and search infrastructure strategy across Relativity’s platform.

• Operate at the intersection of retrieval science, AI/ML, and large-scale distributed systems, with scope across teams and domains.

• Work in a high-trust, action-oriented environment with autonomy, purpose, and room for deep technical exploration.

• Collaborate with product, platform, and AI leaders to deliver next-generation intelligent retrieval experiences for our customers.

• Join a stable, cloud-native organization investing heavily in platform engineering and AI-enabled search architectures.

Benefit Highlights

• Comprehensive health, dental, and vision plans

• Parental leave for primary and secondary caregivers

• Flexible work arrangements

• Two, week-long company breaks per year

• Unlimited time off

• Long-term incentive program

• Training investment program

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$174,000 and $262,000

The 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. 

Required Skills:

Algorithms, Automation, Debugging, Distributed Systems, Performance Tuning, Problem Solving, Project Management, Software Development, System Designs, Technical Leadership