Senior Software Engineering Lead

Posting Type

Hybrid

Job Overview

We are excited to invite a talented Senior Software Engineering Lead to join our dynamic Retrieval Ingestion Team at Relativity. This is the perfect opportunity for an experienced engineer who excels in designing and managing high-throughput ingestion pipelines that efficiently transform raw documents into ready-to-search indexes, even at a massive scale.

As the technical lead, you will take charge of the entire ingestion lifecycle—from the initial content acquisition and normalization through to indexing, enrichment, and ongoing monitoring. You will lead the team in creating robust systems that ensure billions of documents remain both discoverable and searchable in real time. This role combines hands-on technical work with valuable leadership responsibilities. You will mentor team engineers, shape best practices for distributed ingestion, and ensure our efforts align with platform-wide retrieval and search goals.

Job Description and Requirements

• Lead the Retrieval Ingestion Team, offering technical direction, mentorship, and coordination across projects.
• Design, build, and maintain scalable ingestion pipelines capable of reliably processing billions of documents.
• Encourage the adoption of event-driven and micro-batch ingestion frameworks, utilizing tools like Kafka, Kinesis, or Flink.
• Work collaboratively with retrieval engineers to optimize ingested data for indexing and retrieval performance, focusing on sharding, metadata enrichment, and incremental updates.
• Establish SLAs and implement monitoring tools for ingestion metrics such as throughput, latency, data completeness, and recovery parameters.
• Team up with platform, security, and compliance groups to ensure ingestion pipelines securely handle sensitive legal data and meet industry standards.
• Promote best practices in CI/CD, observability, automated testing, and operational readiness for ingestion systems.
• Drive innovation by integrating vector indexing, knowledge graph enrichment, and AI-driven pipelines into the ingestion workflow.

• 6+ years of professional software engineering experience, with at least 2 years in a lead role.
• Expertise in developing distributed ingestion or ETL systems for search or large-scale data platforms.
• In-depth understanding of indexing/search systems (Elasticsearch, Lucene, Solr, Vespa, or OpenSearch).
• Strong programming capabilities in C#, Java, Python, or Go, with a focus on system reliability and performance.
• Familiarity with schema evolution, metadata modeling, and techniques for handling semi/unstructured data for indexing purposes.
• Hands-on experience with Kubernetes, containerization, and CI/CD pipelines in cloud settings (Azure, AWS, or GCP).
• A solid background in observability and operational resilience specific to ingestion systems.

• Experience in integrating embeddings and vector databases into ingestion workflows.
• Familiarity with knowledge graph enrichment, particularly for entity and relationship extraction during the ingestion process.
• A background in regulated industries such as legal, finance, or healthcare.
• Experience with change data capture (CDC) and event sourcing methodologies.
• Contributions to open-source projects related to ingestion, indexing, or retrieval frameworks.

Why Join Us?

• Lead the Retrieval Ingestion Team, significantly impacting how documents are processed for Relativity's cutting-edge search platform.
• Work at the forefront of retrieval, distributed systems, and AI, ensuring that billions of documents are efficiently and securely indexed.
• Mentor fellow engineers and help establish best practices for ingestion and indexing throughout the organization.
• Be part of a cloud-native engineering culture focused on building scalable, AI-enhanced retrieval systems that revolutionize legal data discovery.

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

This position offers a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between:

The final offered salary will depend on various factors including the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring near the top end of the range would be atypical to allow for future meaningful salary growth in this position.