Data Extraction Co-Op: Structure Scientific Knowledge

Lila Sciences, Inc.; Cambridge, San Francisco (MA, CA); On-site; USD 28,000 - 41,000; Part time

Job summary

Lila Sciences, Inc. is seeking a Data Extraction Co-Op in Cambridge, MA and San Francisco, CA to help turn unstructured scientific knowledge into structured signals that power downstream Lila applications.

You will fine-tune models for data extraction, build pipelines, and contribute to production-ready code. Collaborate with researchers and engineers, document findings, and potentially contribute to publications or open-source projects.

Qualifications

• Pursuing a Bachelor's, Master's, or PhD in CS, 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.

Responsibilities

• Fine-tune and evaluate language, multimodal, or specialized models for scientific data extraction.
• Build retrieval and RAG pipelines surfacing 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.

Skills

Machine Learning basics

Python

NLP

Communication

Education

Bachelor's/Master's/PhD in CS, chemistry, materials science, physics, biology, or chemical engineering

Job description

Lila Sciences, Inc. is seeking a Data Extraction Co-Op in Cambridge, MA and San Francisco, CA to help turn unstructured scientific knowledge into structured signals that power downstream Lila applications.

You will fine-tune models for data extraction, build pipelines, and contribute to production-ready code. Collaborate with researchers and engineers, document findings, and potentially contribute to publications or open-source projects.