San Francisco, CA
This role is about investigating the fundamental questions of intelligence, knowledge and understanding in order to develop software with human level intelligence. You will collaborate internally and externally with other researchers, and be supported by a team of research engineers.
Research areas of interest
• Unsupervised RL
• Self-supervised learning
• Multi-task RL
• Continual learning
• Deep learning theory
• Human-like learning
• Network architecture search
• A highly accomplished machine learning researcher (e.g., a track record of high quality papers at top conferences like NeurIPS, ICML, ICLR, etc., or equivalent accomplishments in industry).
• Able to create research questions that clarify the nature of the problem being solved, and coordinate a research program to successfully answer those questions.
• Extremely comfortable running ML experiments.
• Able to clearly communicate about your ideas and intuitions.
• Excited to mentor a small team of great research engineers.
• Work directly on answering the fundamental questions of intelligence, learning, and knowledge, free from politics and pressures to publish or commercialize your research.
• Generous compensation, equity, and benefits.
• Actively co-create and participate in a positive, intentional team culture.
• Frequent team events, dinners, off-sites, and hanging out.
• $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.
How to apply
All submissions are reviewed by a person, so we encourage you to include a cover letter. If you have any other work that you can showcase (open source code, side projects, etc), you should certainly include it!
We try to reply either way within a week or two at most (usually much sooner).
We know that talent comes from many backgrounds, and with many different skills and preferences. That’s why we have a hiring process that gives you the ability to showcase yourself in a variety of different ways, depending on what feels best for you.
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.