This goo was created with a model on Replicate: andreasjansson/plasma
Replicate lets you run machine learning models in the cloud. We’re not just another AI company; we’re a team of developers, engineers, and innovators from organizations like Docker, Spotify, Dropbox, GitHub, Heroku, NVIDIA, and more. We’ve built foundational technologies like Docker Compose and OpenAPI, and now, we’re applying that expertise to make AI deployment as intuitive and reliable as web deployment.
The Models team at Replicate builds models on Replicate that are reliable, fast, and feature complete, ensuring that Replicate has cutting edge open source models for all AI applications.
About you:
You’re a machine learning engineer who is an expert at image, audio, and video models. Making them fast, customizing them, making them controllable, inventing new techniques.
You’re a strong software engineer and have at least 5 years of full time experience. You know the good tools and aren’t just using single letter variable names.
You don’t need a PhD, but you need to understand math for machine learning and be able to parse a research paper.
What you’ll be doing:
We have a huge library of models on Replicate. You’d be making sure they have all the latest features and are fast and reliable.
You’ll write training code so that Replicate users can train their own LoRAs to fine-tune open-source models to fit their needs.
You’d find the latest papers, turn them into useful products, and publish them on Replicate first. You might do some new research, too.
You’d be using cutting edge techniques to empower and enable users to fine tune open source foundation models.
These aren’t hard requirements, but we definitely want to talk with you if…
You’ve invented some new techniques and put them on GitHub.
You’re an expert at PyTorch, down to its internals, using torch.compile(), and so on.
You know how to run a model on multiple GPUs with tensor parallelism.
You’re involved in the generative AI community and are in the right Discords.
This role can be remote (anywhere in the United States) or in-person. We have a preference for timezones closer to PST. If possible, we like people to come into our San Francisco office at least 3 days a week.