Co-Op, Data Extraction

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Co-Op, Data Extraction Cambridge, MA USA Your Impact at LILA Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team works on turning unstructured scientific knowledge (e.g., literature, patents, 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 get hands-on experience fine-tuning and evaluating extraction models, building pipelines for messy real-world data, and shipping work that flows into production systems. What You'll Be Building - Contribute to AI systems that extract and structure knowledge from scientific literature and patents, focused on a well-defined sub-problem - Fine-tune and evaluate language, multimodal, or specialized models for data extraction, with mentor guidance - Build and test pipelines that structure unstructured scientific data across text, tables, and visuals - Run extraction pipelines, analyze results, and document findings clearly - Share your work through a team presentation, write-up, or contribution to a publication or open-source project What You'll Need to Succeed - Pursuing a Bachelor's, Master's, or PhD in Computer Science, Chemistry, Materials Science, or a related field - 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 Bonus Points For - Coursework or projects involving multimodal models or document understanding (OCR, table/figure extraction) - Experience working with messy, real-world datasets

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