Co-Op, Structuring Scientific Data for Discovery
Core
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.
Role type
Co-op machine learning engineer (scientific data extraction)
Builds
Retrieval and RAG pipelines surfacing relevant documents, tables, and figures; structured datasets extracted from scientific literature and patents
Domain
Life sciences, physical sciences, AI for science
Deliverable
production ML models
Required skills
Machine learning fundamentals, Python, NLP concepts, computer vision concepts
Preferred skills
Multimodal models, document understanding (OCR, table and figure extraction), LLM applications, embeddings, retrieval systems (chunking, reranking, vector search), evaluation sets, annotation workflows, scientific document parsing
