Software Engineer, Data Systems

From humanoid robots to autonomous vehicles, every Physical AI model is trained on petabytes of video, lidar, radar, and sensor data. Today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not video corpora. And understanding that video still means paying a person to watch it, ten dollars an hour of footage at the low end. So teams check a sample and hope it represents the rest. The footage grows every year; the budget to look at it doesn't.

Eventual was founded in 2022 to close that gap. Our open-source engine, Daft , is purpose-built for multimodal AI: 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at companies like Mobileye, TogetherAI. On top of it we're building the infrastructure that finds any situation you can describe across a fleet's entire video history, and turns it into a training set or an alert someone can still act on. We fine-tune and run the vision models ourselves, which makes indexing every hour cheaper than annotating a sample.

We're building this with the top Physical AI labs and GPU cloud providers. We've raised $30M from investors like Felicis, CRV, Y Combinator, and angels from the co-founders of Databricks and Perplexity. Our team comes from AWS, Lyft, and Tesla. We powered the last generation of Physical AI in self-driving; now we're doing it for the next.

Join our small (but powerful!) team, 4 days/week in our SF Mission District office.

Our goal is to build Scenario Mining and Data Curation for robot fleet data. We empower Physical AI and robotics teams to instantly find, curate, and stream the data they need to train frontier models.

Eventual is an agile team where every engineer has high ownership across the stack from our compute infrastructure, to our data storage/querying layers and model training/deployment.