About the role
About Eventual Every breakthrough Physical AI system — humanoid robots, autonomous vehicles, video generation models — is trained on petabytes of video, lidar, radar, and sensor data. But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics. They don't know how to index a clip by content, co-locate sensors on the same row as video, version multimodal datasets, or push predicates down to a corpus of MP4s. Robotics and video-AI teams build the missing layer themselves: stitching together five to eight tools, organizing disorganized video and sensor data, building schemas and versioning that don't exist. "It was rebuilding what Databricks built 15 years ago for analytics — just for AI data." Eventual was founded in 2022 to ship that layer once. Our open-source engine, Daft , is the distributed data engine purpose-built for multimodal AI — already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens. We are building a multimodal warehouse on top of our engine for Physical AI: video, sensors, and sim outputs co-indexed on the same row, aligned on timecode, and versioned — with a content-aware query layer on top. We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today. We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc , and angels from the co-founders of Databricks and Perplexity. We've assembled a world-class team from AWS, Render, Pinecone and Tesla. We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next. Join our small (but powerful!) team working together 4 days/week in our SF Mission district office. Your Role As a Storage Infrastructure Engineer, you'll take everything we know about modern databases and apply it to the world of Physical AI. Our warehouse co-indexes video, sensors, embeddings, and sim outputs on the same row, versioned, with a third query layer (not row/column, not vector/semantic) — content-aware queries over what's inside clips. Your job is to make that layer fast: the right indices for petabyte-scale video, predicate pushdowns that elide whole files, file formats that respect random access into clips, and a query path that turns "left-arm grasp failures on deformable objects" into the smallest possible read. You should believe, in your bones, that the best read is the read elided. Key
