MinIO
Principal Product Marketing Manager
Remote, US · Principal
Sponsorship not specifiedDetected 6 days ago
Machine LearningData EngineeringSalesCommunicationPublic Speaking
About the role
- Built for the speed, scale, and economics that AI and analytics demand, AIStor and MemKV unify every layer of the data stack, from agentic and inference context memory to tables and objects across core, edge, and cloud.
- Trusted by 77% of the Fortune 100, MinIO is redefining how AI factories, intelligent applications, and autonomous agents secure, persist, and unlock the full value of their data.
Responsibilities
- AIStor Product Narrative and Positioning: Own the messaging and positioning that defines AIStor as the data foundation for enterprise AI.
- Establish AIStor's role as primary storage for objects and tables across the full AI lifecycle, and keep that narrative consistent across web, sales, analyst, and partner surfaces.
- Messaging and Differentiation: Develop clear, technically grounded messaging that separates AIStor from alternatives on performance, scale, simplicity, and standards-based, no-lock-in architecture.
- Product Launch Execution: Drive go to market strategy and execution for AIStor launches and major feature releases.
- Solutions Marketing for AIStor Use Cases: Build solutions content that shows how AIStor solves specific customer problems, including AI training data lakes, large-scale analytics and table workloads, multimodal data pipelines, and serving as the high-throughput data foundation under modern inference stacks.
- Competitive Enablement: Own the competitive program for AIStor.
- Build and maintain battlecards, talk tracks, and teardown content that give the field a defensible, accurate view of how AIStor compares, with claims that stand up under scrutiny and do not boomerang onto MinIO's own positioning.
- Field Enablement: Build and deliver the enablement content (battlecards, talk tracks, customer presentations, technical briefs) that lets sales engineers and account teams confidently sell AIStor to AI infrastructure buyers, platform engineers, and data platform owners.
- Direct experience marketing a technical product against a defined competitive set, including building battlecards and competitive positioning.
- Own the messaging and positioning that defines AIStor as the data foundation for enterprise AI.
Requirements
- 5+ years of experience in product marketing, with at least 3 years focused on AI/ML infrastructure, data platforms, storage, or other technical infrastructure products.
Skills
- Bachelor's degree in Marketing, Business, Computer Science, or a related field.
- Self-starter mentality with the ability to operate independently in a fast-paced, remote-first environment.
- Hands-on familiarity with S3-compatible storage, object storage, or high-performance data infrastructure for AI and analytics workloads.
- Familiarity with modern AI data pipelines, training and inference workflows, and table or lakehouse formats.
- Background that includes time in technical roles (engineering, solutions architecture, technical pre-sales, or developer relations).
- MinIO is the data and memory foundation for enterprise AI.
Benefits
- Health Care Plan (Medical, Dental & Vision)
- Pre-IPO Stock Options
- Flexible Time Off
Equal opportunity
- Policy (EEO)
- MinIO is proud to be an equal opportunity workplace and an affirmative action employer.
- We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.
- Equal Opportunity Policy (EEO)
Visa & Work Authorization
- ent without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condit
This listing is sourced directly from MinIO's careers page and normalized into a canonical job model.