Generally Intelligent is an AI research company. We help machines learn to understand the world the way humans do. Our mission is to build human-like general machine intelligence and make it safely accessible in order to foster a more abundant, equitable, and creative human society.

We take a first-principles approach to understanding the fundamentals of learning, starting with simple self-supervised networks solving early evolutionary problems, and increasing complexity incrementally.

In this role you’ll work with our researchers to do cutting-edge deep learning research—conducting experiments, creating infrastructure, and developing tooling & visualizations—with the goal of developing more human-like machine intelligence.

If interested, apply here or on Work at a Startup!

Example projects

• Implement a self-supervised network using contrastive and reconstruction losses.
• Create a library on top of PyTorch to enable efficient network architecture search.
• Implement networks from newly published papers.
• Run massively parallel experiments to understand all variants of an architecture.
• Develop more realistic simulations for training our agents.
• Create visualizations to help us deeply understand what our networks learn and why.

You are

• Passionate about understanding the fundamentals of intelligence.
• Very comfortable writing Python.
• Excited to be a world-class ML engineer.
• A fan of pair programming.
• Passionate about engineering best practices.

Benefits

• Work directly on creating software with human-like intelligence.
• Generous compensation, equity, and benefits.
• $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.
• Actively co-create and participate in a positive, intentional team culture.
• Spend time learning, reading papers, and deeply understanding prior work.
• Frequent team events, dinners, off-sites, and hanging out.

About us

Imbue builds AI systems that reason and code, enabling AI agents to accomplish larger goals and safely work in the real world. We train our own foundation models optimized for reasoning and prototype agents on top of these models. By using these agents extensively, we gain insights into improving both the capabilities of the underlying models and the interaction design for agents.

We aim to rekindle the dream of the personal computer, where computers become truly intelligent tools that empower us, giving us freedom, dignity, and agency to pursue the things we love.

We are on the cutting edge of deep learning - implementing papers, running experiments, and designing architectures.

Our stack is Python, PyTorch, Weights & Biases, and home-grown infrastructure to run experiments across AWS/GCP and our own GPUs.