Data & Retrieval Engineer — End-to-End AI Retrieval

Meitner Energy; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Meitner Energy in the United States is seeking a Data and Retrieval Engineer to own the retrieval layer of its private AI platform. You will drive ingestion, parsing, chunking, and metadata for a large corpus of regulatory and engineering documents.

You will implement hybrid retrieval with dense vector search and sparse keywords, ensure access control before retrieval, and develop the evaluation harness to measure retrieval quality.

Qualifications

• Bachelor's degree in a technical field or equivalent experience
• 3+ years in data or ML engineering with focus on document processing and retrieval systems
• Strong Python and production data-pipeline experience
• Hands-on with production vector databases and embedding model deployment
• Experience with OCR tooling and parsing for complex document collections
• Familiarity with retrieval quality evaluation, hybrid retrieval, and reranking models
• Understanding that access enforcement precedes retrieval in regulated environments
• Ability to work on-site in Dallas.

Responsibilities

• Build and own end-to-end ingestion and processing pipeline for regulatory/engineering documents, including OCR, chunking, and metadata generation
• Implement hybrid retrieval combining dense vector search with sparse keyword search, and tune reranking for engineering/regulatory content
• Maintain a gold retrieval evaluation set and contributions to the platform's evaluation harness
• Ensure access-control labeling at document and chunk level and enforce access before retrieval

Skills

Python

Data pipelines

Vector databases

Embedding models

OCR tooling

Document parsing

Retrieval evaluation

Access control

Regulated environment

Education

Bachelor degree

Job description

Meitner Energy in the United States is seeking a Data and Retrieval Engineer to own the retrieval layer of its private AI platform. You will drive ingestion, parsing, chunking, and metadata for a large corpus of regulatory and engineering documents.

You will implement hybrid retrieval with dense vector search and sparse keywords, ensure access control before retrieval, and develop the evaluation harness to measure retrieval quality.