Co-Op, Structuring Scientific Data for Discovery

Lila Sciences • San Francisco, MA

10d

About The Position

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Its team transforms unstructured scientific knowledge—including literature, patents, and technical reports—into structured signals that support downstream applications. As a Data Extraction Co-Op, you will work with research scientists and engineers on a focused problem in this stack. You will fine-tune and evaluate models for scientific knowledge extraction and retrieval, build pipelines for messy real-world data, and contribute work that flows into production systems.

Requirements

• Pursuing a Bachelor's, Master's, or PhD in computer science, chemistry, materials science, physics, biology, or chemical engineering.
• Solid foundation in machine learning fundamentals and Python.
• Familiarity with NLP or computer vision concepts.
• Curiosity about scientific data and willingness to learn quickly in a research setting.
• Clear written and verbal communication.

Nice To Haves

• Coursework or projects involving multimodal models or document understanding, such as OCR and table or figure extraction.
• Experience with LLM applications, embeddings, or retrieval systems, including chunking, reranking, or vector search.
• Experience building evaluation sets or annotation workflows.
• Experience working with messy real-world data.
• Interest in scientific document parsing.

Responsibilities

• Fine-tune and evaluate language, multimodal, or specialized models for scientific data extraction.
• Build retrieval and RAG pipelines that surface relevant documents, tables, and figures.
• Generate and validate structured datasets extracted from scientific literature and patents.
• Run end-to-end pipelines and analyze results against clear evaluation criteria.
• Document findings and present them to the team.
• Contribute to a publication or open-source project where the work supports it.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed