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 If you're a new grad and the thought of spending the next two years bolting another LLM into another wrapper makes you tired, read on. We build real systems. We move petabytes of video through GPU clusters. We profile flamegraphs. We argue about io_uring and Parquet footers. We write Rust and Python. We talk to robotics researchers at the world's top labs about what's actually broken in their training loop, and then we go fix it. The work is hard, the feedback loops are tight, and the impact lands in customer training runs the same week you ship. As a new grad on our team, you'll work shoulder-to-shoulder with senior engineers on the layers that matter — Daft's distributed query engine, our video-native dataloader, the storage and indexing layer, the visual understanding pipeline, or the product surface researchers actually use. You'll be given real ownership early. You'll get mentorship from engineers who've spent careers on systems at AWS, Tesla, Pinecone, and Render. You'll ship code that runs on the most expensive GPU clusters on the planet. We're looking for the kind of new grad who built something obsessive in undergrad — a database, a game engine, a kernel hack, a custom decoder, a research project that wouldn't quit — not because it was assigned, but because they couldn't stop thinking about it. Key
