Posting Type

Hybrid

Job Overview

We are seeking a Lead Software Engineer to join the Retrieval Ingestion Team at Relativity. This role is ideal for an experienced engineer who thrives on designing and operating high throughput ingestion pipelines that transform raw documents into search-ready indexes at scale.

As the technical lead for the Retrieval Ingestion Team, you will own the ingestion lifecycle—from content acquisition and normalization through indexing, enrichment, and monitoring. You will guide the team in building fault-tolerant, low-latency systems that keep billions of documents discoverable and searchable in real time. You will balance hands-on technical contributions with leadership responsibilities, mentoring engineers on the team, shaping best practices for distributed ingestion, and ensuring alignment with platform-wide retrieval and search goals.

Job Description and Requirements

Key Responsibilities

• Lead the Retrieval Ingestion Team, providing technical direction, mentoring, and coordination across projects.
• Architect and maintain scalable ingestion pipelines that handle billions of documents reliably and efficiently.
• Drive adoption of event-driven and micro-batch ingestion frameworks using Kafka, Kinesis, or Flink.
• Collaborate with retrieval engineers to ensure ingested data is optimized for indexing and retrieval performance (sharding, metadata enrichment, incremental updates).
• Establish SLAs and monitoring for ingestion throughput, latency, data completeness, and recovery.
• Partner with platform, security, and compliance teams to ensure ingestion pipelines handle sensitive legal data securely and meet enterprise standards.
• Champion best practices in CI/CD, observability, automated testing, and operational readiness for ingestion systems.
• Contribute to innovation by incorporating vector indexing, knowledge graph enrichment, and AI-driven pipelines into the ingestion workflow.

Required Skills and Experience

• 6+ years of professional software engineering experience, including 2+ years in a lead role.
• Proven expertise in building distributed ingestion or ETL systems for search or largescale data platforms.
• Deep knowledge of indexing/search systems (Elasticsearch, Lucene, Solr, Vespa, or OpenSearch).
• Strong programming skills in C#, Java, Python, or Go, with emphasis on reliability and performance.
• Familiarity with schema evolution, metadata modeling, and handling semi/unstructured data for indexing.
• Hands-on experience with Kubernetes, containerization, and CI/CD pipelines in cloud environments (Azure, AWS, or GCP).
• Strong background in observability and operational resilience for ingestion systems.

Desirable Skills and Experience

• Experience integrating embeddings and vector databases into ingestion workflows.
• Familiarity with knowledge graph enrichment for entity and relationship extraction during ingestion.
• Background in compliance-heavy domains such as legal, finance, or healthcare.
• Experience with change data capture (CDC) and event sourcing patterns.
• Contributions to open-source ingestion, indexing, or retrieval frameworks.

Why Join Us?

• Lead the Retrieval Ingestion Team, driving how documents flow into Relativity’s next generation search platform.
• Operate at the core of retrieval, distributed systems, and AI, ensuring billions of documents are indexed securely and efficiently.
• Mentor engineers and shape best practices for ingestion and indexing across the organization.
• Join a cloud-native engineering culture investing in scalable, AI-enabled retrieval systems that transform how legal data is discovered.

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:

$150,000 and $224,000

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.