Platform Engineer Intern

Our 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.

As 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.

• Design 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.

• Strong 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.)

• Experience building large-scale distributed systems with high demands on performance, reliability, and observability.

• Background in or experience working with machine learning and generative models.

🏢 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.