About CartesiaOur mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.About the RoleAs a Platform Engineer Intern, you'll have the opportunity to work inside our engineering team building cutting edge machine learning infrastructure and products on the latest AI research.Your ImpactDesign and build low latency, scalable, and reliable model inference and serving stack for our cutting edge SSM foundation models.Work closely with our research team and product engineers to translate cutting edge research into incredible products.Build highly parallel, high quality data processing and evaluation infrastructure for foundation model training. What You BringStrong engineering skills, comfortable navigating complex codebases and monorepos.An eye for craft and writing clean and maintainable code.You're comfortable diving into new technologies and can quickly adapt your skills to our tech stack (Go and Python on the backend, Next.js for the frontend.)Nice-to-HavesExperience building large-scale distributed systems with high demands on performance, reliability, and observability.Background in or experience working with machine learning and generative models.Our Culture🏢 We’re an in-person team based out of San Francisco. We love being in the office, hanging out together, and learning from each other every day.🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way.🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.