Data Engineer, AI & Distributed Systems

Company: Zignal Labs

Location: San Francisco, CA (Remote)

Salary: $120k - $140k per year

Type: Full-time

Remote: Yes

Posted: 2026-08-06

About this role

About Zignal Labs
Zignal Labs’ real-time intelligence technology helps the world’s largest organizations protect their people, places, and position. Analyzing billions of data points in real time, Zignal's AI-powered platform accelerates mission-critical decision making by empowering leaders with contextual situational awareness of the information environment.


Fully remote, with Silicon Valley roots and team members in over 20 states, Zignal serves customers around the world. Learn more at zignallabs.com.


About The Role
We ingest, enrich, and structure massive volumes of unstructured data — from social platforms and news outlets to broadcast media — and turn it into real-time intelligence for our customers.


As a Data Engineer on this team, you'll build and operate the pipelines that make that possible. You'll work on systems that process billions of events a day, and on the data pathways that feed our search, NLP, and AI services. You'll own meaningful pieces of the pipeline end to end, and you'll do it alongside engineers who have been running these systems at scale for years.


This is a hands-on build-and-operate role. You don't need to have designed a distributed system from scratch before — you need to be someone who writes solid code, reasons carefully about data correctness and failure modes, and wants to go deep on streaming and AI infrastructure.


What You'll Do

  • Build and maintain pipelines. Develop and operate batch and streaming pipelines that ingest and enrich high-volume unstructured data. Own components end to end, from implementation through production monitoring.
  • Support our AI systems. Build and extend the data pathways that feed downstream NLP, LLM, and retrieval services — including data preparation, embedding generation, and indexing workflows.
  • Work with search and storage layers. Integrate with and tune our search and vector stores to support semantic search, clustering, and real-time retrieval.
  • ...

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