[Fall 2026] Technology - AI Product Engineering

The Whitney’s Academic Year Internship program offers a semester-long paid internship for undergraduate and graduate students currently enrolled in an accredited academic program. The internship is hybrid, with some onsite work. For Fall 2026, interns commit between 16–21 hours per week over 10–12 weeks for a total of 176 hours. Interns are assigned to a specific department at the Museum for the duration of the internship. For more information, including information on eligibility requirements, please visit our Internships page.

The Whitney seeks a Technology - Project ManagementIntern for the Fall 2026 semester.

Expected Projects & Assignments
The AI Product Engineering Intern will design, prototype, and ship AI-powered tools that augment how museum staff work across curatorial, collections, education, and operations functions. The role pairs hands-on engineering with LLMs and agentic systems with product thinking grounded in real user needs, supporting the Whitney’s digital transformation strategy.

The AI Product Engineering Intern will: 
•    Build prototypes and proofs-of-concept using LLM APIs (Anthropic Claude, OpenAI, open-weight models) to address workflow challenges surfaced through staff interviews  
•    Design and implement agentic workflows that combine reasoning, tool use, and integrations with existing museum systems  
•    Develop retrieval-augmented generation (RAG) pipelines over institutional knowledge sources such as collection records, archives, and internal documentation  
•    Write evaluation harnesses to measure prototype quality, accuracy, latency, and cost; iterate based on results  
•    Practice prompt engineering and structured-output design across diverse use cases  
•    Conduct user research with staff to validate prototypes and inform product direction  
•    Document current workflows and identify the highest-leverage opportunities for AI augmentation  
•    Contribute to internal guidelines around responsible AI use, privacy, and data handling  
•    Help shepherd successful pilots toward broader rollout with appropriate documentation and support  

Skills & Qualifications:
•    Coursework in computer science, information systems, data science, machine learning, HCI, or a related technical field  
•    Hands-on experience building with LLM APIs (Claude, GPT, Gemini, or similar); comfort with prompt engineering and structured outputs  
•    Working knowledge of Python or TypeScript, version control with Git and GitHub, and modern AI coding tools such as Claude Code, Cursor, or Cline  
•    Familiarity with at least one of: agentic frameworks, RAG systems, vector databases, or model evaluation methodologies  
•    Comfort reading API documentation and integrating across services (REST, webhooks, OAuth)  
•    Strong product instincts; ability to scope a problem, ship a rough prototype, and iterate based on user feedback  
•    Excellent communication skills and ability to translate technical possibilities for non-technical collaborators  
•    Curiosity about the frontier of AI capability paired with thoughtful awareness of its limits and risks  
•    Ability to work independently while collaborating across curatorial, education, collections, and operations teams  
•    Interest in modern and contemporary art and museum practice preferred  
•    Prior projects (open source, coursework, hackathons, personal builds) that demonstrate shipped AI work are a strong plus  

Provided Training:
•    Introduction to museum operations, departmental workflows, and the cultural-institution context for AI work  
•    Evaluation methodologies for AI systems, from offline evals to in-product feedback loops  
•    User research techniques for AI-augmented workflows in cultural institutions  
•    Project management practices for shipping AI features under uncertainty  
•    Data privacy, security, and responsible-AI considerations for sensitive institutional data  

Outcomes:
•    A portfolio of working AI prototypes deployed in a real cultural institution  
•    Practical fluency with LLM APIs, agentic patterns, evals, and RAG  
•    Experience translating ambiguous user problems into shipped AI products  
•    Understanding of how AI strategy aligns with an institution’s mission, ethics, and constraints  
•    Cross-functional collaboration skills acro…