Generally Intelligent is an independent research company developing AI agents with general intelligence that can be safely deployed in the real world. Our work combines theoretical understanding of deep neural networks with pragmatic engineering in a way that we believe is critical for responsibly engineering safe AI systems that embody human values.
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. As a remote machine learning engineer, you’ll work very closely with a senior member of our research team on cutting-edge deep learning research, infrastructure, and tooling towards the goal of creating general human-like machine intelligence, such as:
– Implement a self-supervised network using contrastive and reconstruction losses.
– Create a library on top of PyTorch to enable efficient network architecture search.
– Open source internal tools.
– Implement networks from newly published papers.
– Work on tools for simple distributed parallel training of deep neural networks.
– Develop more realistic simulations for training our agents.
– Design automated methods and tools to prevent common issues with neural network training (e.g. overfitting, vanishing gradients, dead ReLUs, etc).
– Create visualizations to help us deeply understand what our networks learn and why.
We are looking for a candidate, who is:
– Very comfortable writing Python.
– Familiar with PyTorch and training deep neural networks.
– Excited to work on open source code.
– Passionate about engineering best practices.
– Self-directed and independent.
– Excellent at getting things done
We can offer you these benefits:
– Work directly on creating software with human-like intelligence