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Program Lead, AI Fellowship

Job in San Antonio, Bexar County, Texas, 78208, USA
Listing for: Hidden Jobs
Full Time position
Listed on 2026-09-29
Job specializations:
  • Education / Teaching
    Academic, Training Instructor / Specialist, Online Teaching
Salary/Wage Range or Industry Benchmark: 120000 - 160000 USD Yearly USD 120000.00 160000.00 YEAR
Job Description & How to Apply Below

Role overview

This remote role, based in Latin America or the US, owns the curriculum and learning design for an AI fellowship program. The Program Lead keeps the syllabus aligned with current industry practice and evolving hiring demand while building and maintaining a strong instructor network.

Responsibilities
  • Own the fellowship syllabus, including learning objectives, curriculum sequencing, and how individual modules fit together
  • Oversee the team developing slides, exercises, projects, and supporting learning materials
  • Set and maintain quality standards across all learning materials and program experiences
  • Review learner outcomes and delivery feedback to drive continuous curriculum improvement
  • Source, interview, evaluate, and calibrate instructors so classroom delivery meets program standards
  • Partner with program operations to ensure logistics and delivery decisions support the learning experience
Requirements
  • PhD in computer science, machine learning, or a related STEM discipline, or equivalent deep technical expertise in AI
  • Proven hands-on experience working with AI/ML, preferably in industry or applied research
  • Experience designing or owning technical curricula for fellowships, intensive programs, universities, or corporate academies
  • Teaching experience in university courses, fellowships, bootcamps, or similar settings
  • Strong content-development skills including clear slides, rigorous exercises, and substantive technical projects
  • Fluent in Spanish and English
Nice to have
  • Experience interviewing or evaluating technical instructors, experts, or educators
  • Comfort working in a dynamic environment with fast iteration and a high degree of ambiguity
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