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 RoleAs 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 Responsibilities
Design and build the storage and indexing layer: row groups, column chunks, secondary indices, vector indices, and the metadata that lets queries skip everything that doesn't matter.
Push the query engine harder — predicate pushdown, projection pushdown, late materialization — across multimodal columns including video, embeddings, and sensor streams.
Choose, extend, or build on top of modern open formats (Parquet, Iceberg, Delta etc) and build our own/contribute upstream where it makes sense.
Build versioning and schema evolution for multimodal datasets so customer data stays reproducible across months of experimentation.
Partner with the Dataloading team on the format-to-loader boundary so an
iceberg.scan(...)translates into the absolute minimum of bytes hitting NVMe.Partner with the Visual Understanding team to land model outputs in the index without an external glue layer.
What we look for
You love thinking about indices. B+ trees, LSM trees, bitmap indices, vector indices, learned indices — you have favorites and you have grudges.
You love thinking about query engines. Predicate pushdown makes you happy. Late materialization makes you happier.
Strong familiarity with the storage hierarchy: cloud object stores, NVMe, block storage, spinning disk, RAM, GPU memory — and the latency and cost of moving between them.
Strong opinions about Parquet — love it or hate it, you've earned the opinion. Same for Iceberg, Delta, Lance, and the other lakehouse formats.
A real love for databases and query systems. You read database papers for fun.
You believe the best read is the read elided.
Nice to have
Background from a storage or table-format team — Lance, Iceberg, Delta, Hudi, Spiral, Snowflake, BigQuery, Databricks Photon, DuckDB, ClickHouse, or similar.
You've attempted to build your own database before. Or, at minimum, fantasized about it in detail.
Experience with Rust or modern C++ for storage engines.
Hands-on time with vector indices (HNSW, IVF, SCANN) or hybrid retrieval systems.
Comfort with the OLAP/lakehouse ecosystem: catalogs, file layout, compaction, manifest formats, time travel.
In-person, tight-knit team — 4 days/week in our SF Mission office.
Competitive comp and meaningful startup equity.
Catered lunches and dinners for SF employees.
Commuter benefit.
Team-building events and poker nights.
Health, vision, and dental coverage.
Flexible PTO.
Latest Apple equipment.
401(k) plan with match.
If you've ever read a Parquet footer for fun and thought "this is so close to what video needs, but yet so far" — we should talk.