Co-Op, Data Extraction

Lila Sciences|11 Jun 2026

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Listed location: Cambridge, MA USA

Work arrangement: onsite. A remote label does not confirm worldwide eligibility or visa sponsorship.

Read the employer’s description for qualifications, compensation and work eligibility. Confirm the position is still open on the application page.

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Job Description

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
Interest in scientific document parsing

About LILA

Lila Sciences is building Scientific Superintelligence™ 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 Factory™ 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.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

Frequently asked questions

Is the Co-Op, Data Extraction position at Lila Sciences remote?

The Co-Op, Data Extraction role at Lila Sciences does not have a confirmed remote arrangement in our data. Check the employer description for its work location.