Co-Op, Structuring Scientific Data for Discovery

Alewife, Cambridge, MAfull timeOn Site

H1B: no

Lila Sciences The Data Extraction Co-Op at Lila Sciences fine-tunes and evaluates models for scientific knowledge extraction and retrieval, builds pipelines for real-world data, and ships work into production systems. The role supports scientific discovery by structuring unstructured scientific knowledge.

Responsibilities

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Our team turns unstructured scientific knowledge, including literature, patents, and technical reports, into structured signals that power downstream Lila applications.
As a Data Extraction Co-Op, you will work alongside research scientists and engineers on a focused sub-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 ship work that flows into production systems.
What You'll Be Building
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

Qualification

Pursuing a Bachelor'sClear written and verbal communicationExperience with LLM applicationsInterest in scientific document parsingGuided by our core values of truth

Required

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

Preferred

Coursework or projects involving multimodal models or document understanding (OCR, table and figure extraction)
Experience with LLM applications, embeddings, or retrieval systems (chunking, reranking, vector search)
Experience building evaluation sets or annotation workflows
Experience working with messy real-world data
Interest in scientific document parsing
Lila Sciences is building Scientific SuperintelligenceTM to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science FactoryTM instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

Benefits

Vision

Company

Lila Sciences

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