AI Engineer

About Elicit

Elicit is an AI research assistant that uses language models to help researchers figure out what’s true and make better decisions, starting with common research tasks like literature review.

What we're aiming for:

Visit our Twitter to learn more about how Elicit is helping researchers and making progress on our mission.

AI engineering is a new category of technical work. Its emergence is linked to the increasing availability of powerful new artificial intelligence tools: notably generative machine learning systems like large language models.

The term was suggested by swyx on the Latent Space podcast and we've written about it on our blog before.

Elicit predicted the rise of powerful ML systems even before GPT‑1 was trained, and has the deepest experience of building trustworthy, capable, and transparent applications using these tools.

If you're a software engineer who relishes the more difficult parts of backend code—like concurrency, fault‑tolerance, and distributed systems—you could be a great AI engineer.

Since launching the newest version of Elicit last fall, response has been strong. We introduced Elicit Plus, our monthly subscription plan, and added thousands of paying users in a matter of months as well as hundreds of thousands of new sign‑ups. This has been energizing for our team, but we want to ship more useful functionality to our users even faster.

We believe that building great AI‑powered products requires excellence across multiple parts of the tech stack: from frontend UX to infrastructure. But one of the crux areas is certainly how we prompt, invoke, respond to, and manage the suite of different ML models required to make Elicit work. This is what an AI engineer will be responsible for at Elicit.

• Backend:
  Node and Python.
• Frontend:
  Next.js and Type Script (we expect you to be 80+% focussed on backend work, however).
• We like static type checking in Python and Type Script.
• All infrastructure runs in Kubernetes across a couple of clouds.
• We use Git Hub for code reviews and CI.

Consider the questions:

• What's the difference between anyio, trio, and asyncio?
• What does the await keyword do in JavaScript?
• What is a Kubernetes pod, and how is it different from a container?
• How would you manage state when using an LLM to power a conversation?

If you have a solid answer for these—without reference to documentation—then we should chat!

• Backend implementation of our "living document"
  • We believe that user interactions with language models should be much deeper than yet more chatbot interfaces.
  • We wrote about our living document approach which is one way in which users can have much richer LLM‑powered product experiences.
  • You would work on, curate, extend, and improve the backend part of that technology. This is fascinating and challenging distributed systems work.
• Building Elicit into a product researchers can’t live without
  • We ship useful, exciting features out to users on a weekly basis. Your focus will be on the code which exists between the BFF endpoints and the ML models we use.
  • You will work on a mix of known features / fixes, prototypes to validate ideas, and exploratory projects in between.
  • Our team is small, so we expect you to appreciate the user needs underlying everything you work on. You should be comfortable making decisions and trade‑offs that help us fulfill users’ needs best.
• Keeping Elicit’s bar for quality high
  • You’ll balance shipping features in the short term with building extensible and maintainable systems.
  • You will be responsible for your features in production: they need to be scalable,…