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.
Note: This research role requires being onsite in San Francisco. If you're remote, please take a look at our Machine Learning Engineer (Remote) role, which is more engineering-heavy.
• 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.
• 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.
• 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.
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).
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.