We believe there is an opportunity to create a future where developers are amplified, not automated. This is why we are building the leading open-source AI code assistant and commercializing an enterprise governance layer on top of it to enable organizations to create their own custom AI code assistant.

Headquartered in San Francisco, Continue (YC S23) is funded by Heavybit and angels like Julien Chaumond (co-founder of Hugging Face), Lisha Li (founder of Rosebud AI), and Florian Leibert (co-founder of Mesosphere).

With 18k+ GitHub stars, 500k+ downloads, and many large organizations like Siemens rolling out Continue, we are building a team to tackle the biggest challenges at the intersection of AI and developer tools.

Continue is seeking an intense and fast-learning engineer to help us build our open-source product, starting by resolving and learning about user problems on GitHub and Discord. In this role, you will shape our open-source IDE extensions by designing, building, and maintaining a product loved by many, including yourself.

About you

Please keep in mind that we are describing the background that we imagine would best fit the role. If you don’t meet all the requirements, but you are confident that you are up for the task, we absolutely want to get to know you!

• You are proficient in TypeScript and have a track record of building side projects
• You are a rigorous thinker capable of running disciplined experiments and tackling difficult algorithmic problems
• You are extremely detail-oriented and care deeply about building a product that you are proud to share with others
• Your natural instinct is to jump into the code, which you can do already, since a lot of the codebase is open source

What you will do

We’re a startup, so you’ll have to be ready to do whatever is required to accomplish our mission. However, you can definitely expect to:

• Play a key role in our GitHub and Discord communities by answering questions and resolving problems
• Iterate on the smallest details to make our chat and autocomplete experiences awesome
• Rapidly experiment and design benchmarks to improve autocomplete and codebase retrieval
• Design and implement new affordances for coding with LLMs as new model capabilities emerge