Replicate
Run machine learning models in the cloud
Account Executive
About the role
The best developer businesses are built bottoms-up. Developers pick the best tool for the job, swipe their cards, then grow out from there. AWS, GitHub, Twilio — it’s how you build an enduring developer business.
Replicate’s bottoms-up motion is working like a charm. We have 3.7 million users, which has translated into tens of millions of dollars in revenue. Loads of AI startups are building their entire businesses on top of us, and we’ve got our foot in the door of some of the biggest companies in the world.
The next step for us is selling into this: identifying promising customers, getting them into production, and then expanding. This is starting to work — we’ve got loads of our customers on enterprise contracts, but we now need to dial it in and scale it up.
We’re looking to hire a senior account executive who’s an expert in sales, but also willing to get into the weeds.
Here’s what’s important:
- You should be an expert at the sales process. Identifying customers, building relationships, delivering sparkling demos, and closing deals. Building playbooks, looking after the CRM, and reporting to the company. The whole thing.
- You can talk to engineers. You don’t need to be an engineer, but you do need to be technical enough that you sound credible to an engineer or technical founder.
- You need founder energy. There’s no structure that you’ll be playing in here. You’ll be figuring it out from scratch. You’re going to need to be driven, self-sufficient, and creative. You’ve probably been an early hire at a startup already, or maybe you’ve even been a founder.
You will be pairing with Ben, the CEO. Ben knows the product and our customers, you know how sales works. Together, you’ll find the right product to focus on, who to sell it to, and how to scale it out.
About Replicate
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 bunch of hackers, engineers, researchers, and artists.
We obsess about the details of API design and the right words for things. We're defining how AI works so we'd better get it right.
We make fast and reliable infrastructure. That's what a good infrastructure product is. We're not afraid to build things from scratch to make it the fastest.
We use AI for work. We use AI for play. We find unexplored parts of the map and create new techniques ourselves. We open-source it all.
We build in public, for the community. We want AI to work like open-source software so everyone benefits from it.
We're led by engineers. We all write code. (Or, we get ChatGPT to help.) There aren’t any meetings about meetings.
We've worked at places like Docker, Dropbox, GitHub, Heroku, NVIDIA, Scale AI, and Spotify. We've created technologies like Docker Compose and OpenAPI.
We're here to build a big company. We're ambitious and hard-working. We're not here to just build nice things.
