Eventual aims to solve the problem of traditional data platforms being inadequate for processing the massive volumes of multimodal data required for AI breakthroughs, leading to wasted time on brittle infrastructure instead of research and product development.
Requirements
- Strong background in AI/ML and multimodal data processing
- Experience with distributed systems and data engineering
- Open-source contribution experience preferred
Responsibilities
- Work directly with users to build compelling end-to-end demonstrations that showcases Daft as the definitive engine for any modality of data at any scale, turning complex multimodal scenarios into accessible, reproducible examples.
- Develop tutorials, videos, and technical demos showcasing things like: Data Ingest for Multi-modal RAG systems that seamlessly query documents, images, videos, and code repositories for AI assistants
- Develop tutorials, videos, and technical demos showcasing things like: Agent data orchestration enabling AI agents to access and reason over massive unstructured data repositories
- Develop tutorials, videos, and technical demos showcasing things like: Foundation model training pipelines preparing clean, multimodal datasets for LLM and vision model training
- Develop tutorials, videos, and technical demos showcasing things like: Multi-modal generation workflows combining text, image, and video generation with complex data dependencies
- Develop tutorials, videos, and technical demos showcasing things like: Real-time AI assistant data ingestion processing user documents, conversations, and media for personalized AI experiences
- Drive feature requirements and improvements for our open-source project based on user feedback and real-world applications
Other
- Work closely with users to understand multimodal data challenges and translate them into compelling use-cases
- Proven track record creating technical content and speaking at events
- Ability to translate complex technical concepts into clear, actionable guidance
- In-person tight knit team with 4x a week in office
