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, not the multimodal corpora that power AI. As a result, robotics and video-AI teams iterate on model improvement about once a week. Most of that week isn't training — it's finding the right data: writing CV heuristics over raw footage, paying annotators for edge cases, hand-curating clips before a cluster ever spins up. GPU bandwidth has grown 2-3× per generation. Storage and pipelines haven't. The gap widens every year. Eventual was founded in 2022 to close it. 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 video-native index on top of our engine for Physical AI that collapses the data iteration loop. Describe the dataset you want, get a curated table in minutes, feed it to your GPUs at line rate. One iteration per day becomes the norm. 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 Fullstack Engineer, you'll own the product surface that researchers actually touch. Underneath us is a multimodal database indexing petabytes of video, sensors, and embeddings — but a Physical AI researcher experiences our product as the interface where they explore their corpus, write a natural-language query, watch the results come back, sanity-check clips, and ship a curated dataset to a training run. That interface is yours to design and build: visualizations over multimodal data, analytics on dataset composition, query authoring, dataset versioning, and the APIs that let customers integrate any of it into their own training stack. You'll work directly with researchers at our partner labs — your shortest feedback loop is them telling you what they wish they could see in their data. We move fast, ship to production weekly, and care more about whether researchers are actually using the surface than how clean the abstraction is. Key
