At Generally Intelligent, we strive to combine the best elements of high-touch executive recruiting and high-volume tech recruiting. In order to accomplish this, we are looking for a Recruiting Coordinator to give every candidate a white-glove candidate experience throughout our recruiting process.

• Adept at written communication and highly expressive

• Organized and able to stay on top of complex processes

• Efficient and effective at prioritizing many simultaneous tasks

• Skilled and passionate about building, refactoring, and maintaining internal processes

• Highly empathetic and passionate about creating beautiful experiences for people

• Manage the logistics of the candidate pipeline from end-to-end while fostering a positive, high-touch, and personal candidate experience

• Schedule, re-schedule, and adjust candidate interviews, trial projects, and onsites (read more about our process here)

• Answer candidate questions about the interview process

• Manage internal calendars to optimize the team’s time

• Design and automate processes to ease pain points in the coordination process

All submissions are reviewed by a person, so we encourage you to include notes on why you're interested in working with us. If you have any other work that you can showcase (open source code, side projects, etc.), certainly include it! We know that talent comes from many backgrounds, and we aim to build a team with diverse skill sets that spike strongly in different areas.

We try to reply either way within a week or two at most (usually much sooner).

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.