Technical Product Manager
Posted 6 Oct 2026
Advertised as “AI Technical Product Manager, Dean's Office”
BranchFactor Summary
The original posting with the fluff stripped out
- Level
- 058
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
- Visa sponsorship
- Not offered
No salary listed for this role.
Field of studyComputer Science, Data Science, Engineering (or related)
Study levelBachelor's
Study levelMaster's
Company Description
By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.
Why join Harvard Business School?
Harvard Business School, located on a 40-acre campus in Boston, was founded in 1908 as part of Harvard University. It is among the world's most trusted sources of management education and thought leadership. For more than a century, the School's faculty has combined a passion for teaching with rigorous research conducted alongside practitioners at world-leading organizations to educate leaders who make a difference in the world. Through a dynamic ecosystem of research, learning, and entrepreneurship that includes MBA, Doctoral, Executive Education, and Online programs, as well as numerous initiatives, centers, institutes, and labs, Harvard Business School fosters bold new ideas and collaborative learning networks that shape the future of business.
Job Description
Job Summary:
The AI Technical Product Manager bridges the gap between artificial intelligence capabilities and real-world product applications, combining deep technical understanding with strategic product understanding to build AI-powered solutions that deliver measurable value.
Job-Specific Responsibilities:
Product
- Balance innovation with practical implementation, assessing technical feasibility and business impact
- Establish success metrics and KPIs for AI product initiatives
Technical Leadership
- Collaborate with data scientists, ML engineers, and software developers to translate business requirements into technical specifications
- Understand AI/ML fundamentals including model architectures, training processes, evaluation metrics, and deployment considerations
- Make informed decisions about model selection, data requirements, and infrastructure needs
- Evaluate emerging AI technologies and determine their applicability to product challenges and understand risk mitigation strategies.
Cross-Functional Collaboration
- Partner with engineering teams to prioritize features and manage the development lifecycle
- Work with design teams to create intuitive user experiences that leverage AI capabilities effectively
- Coordinate with data engineering on data pipelines, quality, and governance