Founding Engineer at Replicate
About the role
You are a generalist engineer, leaning towards backend/infrastructure. You will basically help us build this thing from scratch. We don't mind what particular skills you already have. We figure you can pick up something new quickly.
When starting this company, we thought: instead of getting a job at the best place to work, let's make that best place to work. We want to work with the best people in an inclusive, supportive environment. And, just have fun while we're at it. You will help us make that place.
We're looking for the right person, not just someone who checks boxes, so you don't need to satisfy all these things. But, you might have some of these qualities:
- Experience building and scaling backend systems and infrastructure.
- Experience building CLI/developer tools, and a sense for what makes a really good tool.
- Excellent communication skills. We think most of being a programmer is not programming. We want you to be able to communicate complex topics clearly, write down your thinking, write good docs, etc.
- Ideally good at public speaking and getting people excited about what we're building.
- Ideally worked on developer tools or ML.
- You really get it. We think we're building something quite exciting here, and we want you to be excited about it too.
- Entrepreneurial and can hit the ground running.
- Kind-hearted and creative.
- Located anywhere. We have a beautiful office in Berkeley, CA where two of us mostly work, and Andreas is mostly based in Europe. We all spend time working remotely.
We want our team to feel invested in what we're building, so we will give the right person a meaningful amount of equity. And, all the usual things. (We're European so you'll get really good healthcare.)
Why you should join Replicate
Machine learning research is really similar to open source software: it's a worldwide community of people sharing new techniques and building upon each other's work. But, the standard way of sharing new work is publishing a PDF on a mailing list from the 90s. (Rather ironically for one of humanity's most advanced technologies.)
Using machine learning inside companies it isn't much better — there's no version control, random files are scattered on S3, nobody knows what's running where.
To fix this, we're building a place where researchers can publish an actual, runnable version of their work. Other researchers can run their work and build upon it. It will be a community of all the best ML researchers.
Inside companies, this will be the place where researchers and engineers store models. Models will be stored in one place in a runnable format, so other people can try them out, build upon them, and deploy them to production.
We're also building the open source infrastructure to make this work: tools for versioning and packaging machine learning models.
Ultimately, we want machine learning to be as collaborative and accessible as open source software. You shouldn’t have to understand complex math or write an academic PDF to do useful things with ML. It should be as easy as forking code on GitHub or importing a package from npm.