Eventual
AI Developer Advocate
Every breakthrough AI applicationfrom foundation models to AI agents to autonomous vehiclesrequires processing massive amounts of images, video, and complex data. But here's the problem: today's data platforms (Databricks, Snowflake, etc) are built on top of tools made for spreadsheet-like business analytics and SQL queries, not petabytes of multimodal information. This forces teams to waste months building complex infrastructure instead of solving the problems that are core to their business. Our cofounders started Eventual in 2022 out of frustration with this exact challenge. Our mission is simple: make querying any type of dataimages, video, audio, textas intuitive as working with tables, yet powerful enough to handle petabytes at production scale. Unlike traditional engines with hardcoded operations, we handle the chaos of real production: coordinating with dozens of external APIs, managing GPU clusters, and turning the 0.1% failure rate that kills traditional systems into reliable execution. Our open-source engine Daft already powers critical AI workloads at companies such as Amazon, Mobileye, TogetherAI, and CloudKitchens, running on 800,000 CPU cores daily. We've assembled a world-class team from Databricks, AnyScale, Tesla, and Lyft with deep expertise in high-performance computing and big data infrastructure. Backed with significant funding from YCombinator, Caffeinated Capital, Array.vc, and top angels from Databricks, Anthropic, Meta, and Lyft, we're building the generational technology that will enable entirely new classes of AI breakthroughs. We're in the process of doubling our team, join us today! Your Role
As our AI Developer Advocate, you will champion AI workload use-cases and drive adoption of Daft, our distributed query engine for multimodal data. You'll 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. When you're not producing amazing developer facing content, your work will also directly influence Daft's roadmap as you learn from our users. Key Responsibilities
User Engagement: Work closely with users to understand multimodal data challenges and translate them into compelling use-cases
Technical Content Creation: 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
Agent data orchestration enabling AI agents to access and reason over massive unstructured data repositories
Foundation model training pipelines preparing clean, multimodal datasets for LLM and vision model training
Multi-modal generation workflows combining text, image, and video generation with complex data dependencies
Real-time AI assistant data ingestion processing user documents, conversations, and media for personalized AI experiences
Community Building: Present at conferences, meetups, and technical events to grow our developer community
Product Development: Drive feature requirements and improvements for our open-source project based on user feedback and real-world applications
What We Look For
Strong background in AI/ML and multimodal data processing
Experience with distributed systems and data engineering
Proven track record creating technical content and speaking at events
Ability to translate complex technical concepts into clear, actionable guidance
Open-source contribution experience preferred
Perks & Benefits
Hybrid work environment - 3x a week in office
Competitive comp and startup equity
Catered lunches and dinners for SF employees
Commuter benefit
Team building events & poker nights
Health, vision, and dental coverage
Flexible PTO
Latest apple equipment
401k plan with match!
Every breakthrough AI applicationfrom foundation models to AI agents to autonomous vehiclesrequires processing massive amounts of images, video, and complex data. But here's the problem: today's data platforms (Databricks, Snowflake, etc) are built on top of tools made for spreadsheet-like business analytics and SQL queries, not petabytes of multimodal information. This forces teams to waste months building complex infrastructure instead of solving the problems that are core to their business. Our cofounders started Eventual in 2022 out of frustration with this exact challenge. Our mission is simple: make querying any type of dataimages, video, audio, textas intuitive as working with tables, yet powerful enough to handle petabytes at production scale. Unlike traditional engines with hardcoded operations, we handle the chaos of real production: coordinating with dozens of external APIs, managing GPU clusters, and turning the 0.1% failure rate that kills traditional systems into reliable execution. Our open-source engine Daft already powers critical AI workloads at companies such as Amazon, Mobileye, TogetherAI, and CloudKitchens, running on 800,000 CPU cores daily. We've assembled a world-class team from Databricks, AnyScale, Tesla, and Lyft with deep expertise in high-performance computing and big data infrastructure. Backed with significant funding from YCombinator, Caffeinated Capital, Array.vc, and top angels from Databricks, Anthropic, Meta, and Lyft, we're building the generational technology that will enable entirely new classes of AI breakthroughs. We're in the process of doubling our team, join us today! Your Role
As our AI Developer Advocate, you will champion AI workload use-cases and drive adoption of Daft, our distributed query engine for multimodal data. You'll 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. When you're not producing amazing developer facing content, your work will also directly influence Daft's roadmap as you learn from our users. Key Responsibilities
User Engagement: Work closely with users to understand multimodal data challenges and translate them into compelling use-cases
Technical Content Creation: 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
Agent data orchestration enabling AI agents to access and reason over massive unstructured data repositories
Foundation model training pipelines preparing clean, multimodal datasets for LLM and vision model training
Multi-modal generation workflows combining text, image, and video generation with complex data dependencies
Real-time AI assistant data ingestion processing user documents, conversations, and media for personalized AI experiences
Community Building: Present at conferences, meetups, and technical events to grow our developer community
Product Development: Drive feature requirements and improvements for our open-source project based on user feedback and real-world applications
What We Look For
Strong background in AI/ML and multimodal data processing
Experience with distributed systems and data engineering
Proven track record creating technical content and speaking at events
Ability to translate complex technical concepts into clear, actionable guidance
Open-source contribution experience preferred
Perks & Benefits
Hybrid work environment - 3x a week in office
Competitive comp and startup equity
Catered lunches and dinners for SF employees
Commuter benefit
Team building events & poker nights
Health, vision, and dental coverage
Flexible PTO
Latest apple equipment
401k plan with match!