Eventual
Building the AI data engine for any modality and scale
About the Company
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!
Tech Stack
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
Founders
Jay is based in San Francisco and graduated from Cornell University where he did research in deep learning and computational biology. He has worked in ML Infrastructure across biotech (Freenome) and autonomous driving (Lyft L5), building large-scale data and computing platforms for diverse industries. Jay is originally from Singapore and spent 2 years as a tank commander in the military and then as the head of recruiting at a unicorn Singapore startup.
Sammy Sidhu is co-founder and CEO of Eventual. Sammy's background is in High Performance Computing (HPC) and Deep Learning and has over a dozen patents/publications in the space. In the past, he has worked on high frequency trading on wall street, medical AI research at Berkeley and self-driving cars at both DeepScale (acquired by Tesla) and Lyft Level 5 (acquired by Toyota). Native to the Bay Area, Sammy graduated from UC Berkeley with a degree in Electrical Engineering and Computer Science.
Open Positions at Eventual (6 Jobs)
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