Laboratory Automation Systems Intern

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Purpose

The Laboratory Automation Systems Intern will support a campus-wide initiative at the Spring House Campus focused on improving the visibility, documentation, and knowledge management of laboratory automation platforms. Working alongside Laboratory Operations, automation specialists, scientists, engineers, and IT/Digital partners, the intern will help inventory and characterize laboratory automation systems, organize and reconcile technical information, and identify practical Large Language Model (LLM) use cases that improve access to automation-related knowledge. This 10–12 week internship provides hands-on exposure to laboratory automation, digital systems, technical data management, responsible AI, and cross-functional collaboration within a scientific laboratory environment.

Responsibilities

• Conducting supervised laboratory walkthroughs to develop and maintain an accurate inventory of automation systems, components, software, locations, system owners, operational status, and scientific use cases.
• Reconciling field observations with existing records and working with system owners to identify and resolve missing, duplicate, or inconsistent information.
• Learning automation platform purpose, operating sequences, inputs, outputs, interfaces, dependencies, and supporting scientific workflows through supervised shadowing.
• Collecting, reviewing, and organizing technical documentation, including equipment manuals, configuration records, setup instructions, deck layouts, integration notes, and service documentation.
• Applying consistent naming conventions and metadata to improve the organization, standardization, and searchability of automation-related information.
• Identifying and defining potential LLM use cases for record classification, metadata extraction, setup summarization, missing-data detection, and other knowledge-management needs.
• Documenting AI use-case objectives, intended users, data inputs, expected outputs, assumptions, risks, success criteria, and human-review requirements.
• Evaluating the feasibility of proposed LLM use cases using approved tools and verified information.
• Supporting automation system assessments, demonstrations, data cleanup, reporting, issue tracking, project coordination, and knowledge-transfer activities.
• Developing standardized automation system profiles and supporting the creation of an organized, searchable repository of equipment and technical information.
• Preparing project documentation and a final stakeholder presentation summarizing completed work, findings, identified gaps, and recommendations.
• Following applicable laboratory safety, EH&S, cybersecurity, data-handling, responsible AI, access-control, and equipment-specific requirements.

Qualifications / Requirements

• Completion of Undergraduate Freshman year at an accredited University is required.
• Currently pursuing a Bachelor’s or Master's degree in Laboratory Automation, Engineering, Computer Science, Data Science, Information Systems, Life Sciences, Pharmaceutical Sciences, Digital Systems, or related fields.
• Have a cumulative GPA of 3.0 or higher, which is reflective of all college coursework.
• Strong attention to detail and ability to organize, validate, reconcile, and document technical information accurately.
• Working knowledge of Microsoft productivity and collaboration tools, including Excel, PowerPoint, SharePoint, and/or Microsoft Teams.
• Strong analytical, organizational, and problem-solving skills.
• Effective written and verbal communication skills.
• Ability to collaborate effectively with scientific, engineering, automation, operations, and IT/Digital stakeholders.
• Ability and willingness to learn unfamiliar laboratory equipment, software, terminology, and scientific workflows.
• Ability to manage priorities, track issues, and complete assigned activities within established timelines.
• Ability to work independently on assigned responsibilities while contributing effectively within a cross-functional team.
• Comfort working under supervision within laboratory environments.
• Willingness and ability to complete required laboratory safety, EH&S, cybersecurity, data-handling, responsible AI, and site-access training.
• Ability to follow established access controls, equipment-specific requirements, and applicable information-security and data-governance practices.
• Permanently authorized to work in the U.S., must not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future.

Preferred Qualifications

• Interest or experience with laboratory automation systems, robotics, automation software, or scientific instrumentation.
• Familiarity with automation system components, equipment interfaces, laboratory workflows, or scientific technology.
• Exposure to asset-management systems, service platforms, or technical equipment databases.
• Experience organizing or documenting technical systems, equipment, or workflows.
• Experience using Excel, SharePoint, and Microsoft Teams to organize, review, or maintain technical information.
• Familiarity with data cleanup, data validation, reconciliation, metadata, record classification, or structured information management.
• Experience working with equipment documentation, manuals, configuration records, setup instructions, or other technical information.
• Interest or experience in knowledge management and improving the accessibility, consistency, and usability of technical information.
• Interest or experience with Large Language Models (LLMs), Generative AI, prompt design, or responsible AI.
• Familiarity with using approved AI tools for activities such as information classification, metadata extraction, summarization, or missing-data identification.
• Understanding of the importance of data quality, information security, human review, and responsible use of AI-generated outputs.