About Eventual Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product.

Eventual was founded in 2022 to solve this. Our mission is to make querying any kind of data, images, video, audio, text, as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t. Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and CloudKitchens.

We’ve assembled a world-class team from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, Tesla, and more, quadrupling our size within a year. With backing from Y Combinator, Caffeinated Capital, Array .vc, and top angels from the co-founders of Databricks and Perplexity, we’re looking to double the team now. Join us—Eventual is just getting started.

Please note we are looking for someone who is willing and able to come into our San Francisco office in the Mission district 4 days / week.

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

Eventual enables developers to build the AI systems of the future by making multi-modal data and models work together seamlessly. Every breakthrough AI application—from foundation models to autonomous vehicles relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) were built for spreadsheet-like analytics, not the petabytes of multimodal data that power AI.

Eventual was founded in 2022 to change that. Our mission is to make working with any kind of data—images, video, audio, text—as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t.

Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and CloudKitchens. Backed by Y Combinator, Caffeinated Capital, Array.vc, and top angels from the co-founders of Databricks and Perplexity, we’re just getting started—and we’d love for you to be part of it.

Please note: This internship is based in our San Francisco office (Mission District). We value in-person collaboration and ask interns to be in the office at least 4 days a week.

Your Role

As a Software Engineering Intern, you’ll work alongside our engineers to contribute to Eventual’s core products and open-source engine. You’ll gain hands-on experience building distributed data systems and products, while learning how to design, implement, and test features that support real-world AI and ML workloads.

You’ll get mentorship from our team (ex-Databricks, AWS, Nvidia, Tesla, GitHub Copilot, Pinecone) and the opportunity to make meaningful contributions that reach production.

Key Responsibilities

• Contribute to building features in Eventual’s distributed query engine.
• Work on developing Eventual’s cloud service.
• Write clean, maintainable code with guidance from senior engineers.
• Collaborate with the team to design and implement solutions for real-world data challenges.
• Assist with testing, debugging, and documenting core system components.

What We Look For

• Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science (or related field).
• Strong programming skills in Python and/or Rust.
• Interest in distributed systems, databases, or data infrastructure.
• Familiarity with cloud technologies (AWS, GCP, or Azure) is a plus.
• Excitement to learn from a fast-paced startup environment and contribute to real-world systems.

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