Internship, AI – Agentic & Machine Learning Development
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Before you apply
LocationRemote US
Work moderemote · United States
Typeinternship
SeniorityEntry
Company size5,001–10,000 people
PostedOct 30, 2026 · 1w ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
Collaborate with cross-functional stakeholders to design and deliver AI and machine learning solutions
Develop, test, and optimize AI agents, prompts, workflows, and machine learning models
Create contextual data layers and orchestration frameworks that improve agent and model performance
Evaluate solutions for accuracy, scalability, and effectiveness, including testing edge cases and failure modes
Document experiments, model performance, agent iterations, and key insights
Skills
AI
machine learning
prompt engineering
About the role
Are you an aspiring professional looking to gain hands-on experience and expand your skill set in a real-world business environment? The AI – Agentic & Machine Learning Development Internship offers an exciting opportunity to apply your knowledge, build practical skills, and contribute to meaningful work.
About the company
At VSP Vision, we’re on a mission to help people see every possibility—and we do that through our strategic business pillars that connect nearly 90 million patients and their health data to network doctors across the globe.
As the AI – Agentic & Machine Learning Development Intern, you’ll partner with experienced professionals and support a dynamic team across key functions. This role provides exposure to the day-to-day operations of a multi-faceted organization while helping you develop critical business competencies.
Responsibilities
Collaborate with cross-functional stakeholders to design and deliver AI and machine learning solutions that address business needs
Develop, test, and optimize AI agents, prompts, workflows, and machine learning models using structured and unstructured data
Create contextual data layers and orchestration frameworks that improve agent and model performance
Evaluate solutions for accuracy, scalability, and effectiveness, including testing edge cases and failure modes
Document experiments, model performance, agent iterations, and key insights to support continuous improvement and knowledge sharing