Job description

About the company Our client builds AI-powered legal software for research and litigation workflows. Its products use large collections of court records and legal documents to help turn complex information into structured insights. The role / why it matters You will own the data pipelines that ingest, normalize, and enrich caselaw and docket records. Legal source data is messy, often arriving as scanned filings or semi-structured documents. The reliable signal you create will support downstream research and AI products. What you'll do • Build and maintain production pipelines for PACER, NYSCEF, state court systems, published opinions, and other legal records. • Turn PDFs, scanned filings, XML, and HTML into clean, queryable data. • Create LLM-assisted extraction workflows for legal text and validate results against source documents. • Run statistical analyses, improve data quality, and optimize SQL queries. • Support RAG and semantic search workflows in partnership with full-stack engineers. • Operate and improve AWS S3 and RDS data systems within established security and compliance guardrails. What we're looking for • 4-8 years of experience building and operating production data pipelines with large, dirty datasets, and quickly finding signal in them. • Strong Python and SQL skills for pipeline engineering and data analysis. • Hands-on experience with legal data such as PACER records, court dockets, or court filings. • Experience with AI for data, including RAG, semantic search, and LLM-assisted extraction, plus PDF parsing, OCR, and document extraction tooling. • A bachelor's degree in Computer Science, Data Science, or a related field. • Works fully autonomously and thrives with minimal management direction; stays curious about messy data problems and critically reviews AI-assisted code and outputs. Bonus points • Experience with regulatory or government data sources. • Experience with vector databases or semantic-search tooling such as Pinecone or Voyage AI. Compensation and benefits • Base salary: $180,000-$220,000 USD. • Equity: 0.15%-0.25%. Location / work model • Remote within the United States, with at least four hours of overlap with Eastern Time each workday. • Strong preference for candidates based in the New York City area. • Plan for quarterly travel to New York City. • Visa transfers may be considered. New visa sponsorship is not available.

Requirements

Must-haves

Build and maintain production pipelines for PACER, NYSCEF, state court systems, published opinions, and other legal records.
Turn PDFs, scanned filings, XML, and HTML into clean, queryable data.
Create LLM-assisted extraction workflows for legal text and validate results against source documents.
Run statistical analyses, improve data quality, and optimize SQL queries.
Support RAG and semantic search workflows in partnership with full-stack engineers.
Operate and improve AWS S3 and RDS data systems within established security and compliance guardrails.
4-8 years of experience building and operating production data pipelines with large, dirty datasets, and quickly finding signal in them.
Strong Python and SQL skills for pipeline engineering and data analysis.
Hands-on experience with legal data such as PACER records, court dockets, or court filings.
Experience with AI for data, including RAG, semantic search, and LLM-assisted extraction, plus PDF parsing, OCR, and document extraction tooling.
A bachelor's degree in Computer Science, Data Science, or a related field.
Works fully autonomously and thrives with minimal management direction; stays curious about messy data problems and critically reviews AI-assisted code and outputs.

Nice-to-haves

Experience with regulatory or government data sources.
Experience with vector databases or semantic-search tooling such as Pinecone or Voyage AI.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmsqlawspython

Platform: workable
Effort: medium