Remote and San Francisco, CA /
In order to develop systems with more human-like intelligence, formulating the right tasks and training in an environment that leads to generalizable intelligence is of the utmost importance. As a starting point, we have built Avalon, an open-source, open-world 3D environment for training AI agents in a setting that is more similar to the environment in which humans evolved. In this role, you will iterate on and improve Avalon so that we(and other researchers) may use it as a benchmark for AI capabilities.
• Add support for worlds with multiple learned agents
• Add new types of procedurally generated worlds to test for new skills
• Add basic spatial audio features to support specification of tasks via language
• Extend Avalon to support new research use cases(ex: within-lifetime learning, new forms of generalization, etc)
• Add more complex physics and object interactions(ex: breaking objects apart and combining them)
• Optimize simulator to improve latency and throughput for RL training
• Very comfortable writing in one of Python, C#, C++, or C
• Excited to work on open-source code
• Self-directed and independent
• Comfortable operating in an asynchronous, distributed environment
• Experience developing 3D games or simulation environments
• Experience with Godot
• Work directly on creating software with human-like intelligence
• Very generous compensation
• Flexible working hours
• Work remotely
• Time and budget for learning and self-improvement
How to apply
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 skillsets 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 primarily on self-supervised and generative video and audio models. 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.