Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI.

Our product is open-source and used at enterprise scale

Our distributed data engine Daft is open-sourced and runs on 800k CPU cores daily. This is more compute than Frontier, the world's largest supercomputer!

Today's data tooling (Spark, Presto, Snowflake) was built for a world of tabular data analytics, but does not generalize to the needs of modern ML/AI such as multimodal data, heterogenous compute and user-defined Python algorithms.

Eventual and Daft bridge that gap, making ML/AI workloads easy to run alongside traditional tabular workloads.

We're growing - come grow with us!

We are well funded by investors such as YCombinator, Caffeinated Capital, Array.vc and top angels in the valley from Databricks, Meta and Lyft.

Our team has deep expertise in high performance computing, big data technologies, cloud infrastructure and machine learning. Our team members have previously worked in top technology companies such as AnyScale, Tesla and Lyft.

We are looking for exceptional individuals with a passion for technology and a strong sense of intellectual curiosity.

If that sounds like you, please reach out even if you don't see a specific role listed that matches your skillsets - we'd love to chat!

About Eventual

Every breakthrough AI application—from foundation models to AI agents to autonomous vehicles—requires 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 data—images, video, audio, text—as 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 Anthropic, Amazon, Mobileye, TogetherAI, and CloudKitchens.

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:

• 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!

Our stack is a mix of Python and Rust. We integrate closely with distributed systems such as Ray and storage engines such as Apache Iceberg and AWS S3.

Examples of domains that we work in include:

• Databases: Daft employs modern query planning and optimizations for efficient execution of distributed workloads
• Enterprise Big Data: Daft integrates with data lake technologies such as Apache Iceberg and Hive, with record-setting I/O throughputs to these storage formats
• Low-Level System Programming: The execution core of Daft is written in Rust for memory-efficient data processing