What we're doing

Machine learning can now do some extraordinary things: it can understand the world, drive cars, write code, make art.

But, it is still extremely hard to use. Research is typically published as a PDF, with scraps of code on GitHub and weights on Google Drive (if you’re lucky!). It is near-impossible to take that work and apply it to a real-world problem, unless you are an expert.

We’re making machine learning accessible to everyone. People creating machine learning models should be able to share them in a way that other people can use, and people who want to use machine learning should be able to do it without getting a PhD.

With great power also comes great responsibility. We believe that with better tools and safeguards, we will make this powerful technology safer and easier to understand.

How we work

We're a kind, creative, hard-working bunch. We care about our work and our users. We're humble and show humility. We're looking for the same in the people we work with.

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.

You can be located anywhere. We have a beautiful office in San Francisco, CA (specifically The Mission) where some of us work, but we operate as a remote-first company across American and European timezones.

We want our team to feel invested in what we're building. We pay market salary, but well-above market equity. And, all the usual things. (We're European so you'll get really good healthcare.)

You’re a machine learning engineer who is an expert at productionizing and optimizing models.

We have a huge library of community-contributed machine learning models. You’ll maintain some of the most popular ones so they’re fast and reliable.

It’ll involve implementing open-source models, optimizing them, and doing general maintenance on them. It’s part ML engineer, part open-source gardener.

We’re looking for the right person, not just someone who checks boxes, so you don’t need to satisfy all of these things. But, you might have some of these qualities:

A balance of software engineering and machine learning skills.

• You can squeeze every last drop of performance out of a GPU.
• You’ve worked with model compression techniques like pruning and distillation.
• You know your way out of CUDA error: device-side assert triggered.
• Ideally you’re involved in the generative AI community and familiar with diffusion models and similar techniques.
• You don’t need a PhD or know how to build new architectures from scratch.
• 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.

We have a web product (currently React + Django), an open source CLI (Go + Python), and Kubernetes ML serving infrastructure.